diff --git "a/sf_log.txt" "b/sf_log.txt" --- "a/sf_log.txt" +++ "b/sf_log.txt" @@ -1,50 +1,50 @@ -[2023-02-23 23:11:11,509][00448] Saving configuration to /content/train_dir/default_experiment/config.json... -[2023-02-23 23:11:11,515][00448] Rollout worker 0 uses device cpu -[2023-02-23 23:11:11,517][00448] Rollout worker 1 uses device cpu -[2023-02-23 23:11:11,519][00448] Rollout worker 2 uses device cpu -[2023-02-23 23:11:11,521][00448] Rollout worker 3 uses device cpu -[2023-02-23 23:11:11,523][00448] Rollout worker 4 uses device cpu -[2023-02-23 23:11:11,525][00448] Rollout worker 5 uses device cpu -[2023-02-23 23:11:11,527][00448] Rollout worker 6 uses device cpu -[2023-02-23 23:11:11,529][00448] Rollout worker 7 uses device cpu -[2023-02-23 23:11:11,704][00448] Using GPUs [0] for process 0 (actually maps to GPUs [0]) -[2023-02-23 23:11:11,705][00448] InferenceWorker_p0-w0: min num requests: 2 -[2023-02-23 23:11:11,739][00448] Starting all processes... -[2023-02-23 23:11:11,740][00448] Starting process learner_proc0 -[2023-02-23 23:11:11,792][00448] Starting all processes... -[2023-02-23 23:11:11,805][00448] Starting process inference_proc0-0 -[2023-02-23 23:11:11,805][00448] Starting process rollout_proc0 -[2023-02-23 23:11:11,810][00448] Starting process rollout_proc1 -[2023-02-23 23:11:11,810][00448] Starting process rollout_proc2 -[2023-02-23 23:11:11,811][00448] Starting process rollout_proc3 -[2023-02-23 23:11:11,811][00448] Starting process rollout_proc4 -[2023-02-23 23:11:11,811][00448] Starting process rollout_proc5 -[2023-02-23 23:11:11,811][00448] Starting process rollout_proc6 -[2023-02-23 23:11:11,811][00448] Starting process rollout_proc7 -[2023-02-23 23:11:22,978][11085] Using GPUs [0] for process 0 (actually maps to GPUs [0]) -[2023-02-23 23:11:22,989][11085] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0 -[2023-02-23 23:11:22,991][11099] Using GPUs [0] for process 0 (actually maps to GPUs [0]) -[2023-02-23 23:11:22,994][11099] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0 -[2023-02-23 23:11:23,399][11105] Worker 5 uses CPU cores [1] -[2023-02-23 23:11:23,493][11102] Worker 2 uses CPU cores [0] -[2023-02-23 23:11:23,568][11100] Worker 0 uses CPU cores [0] -[2023-02-23 23:11:23,571][11107] Worker 7 uses CPU cores [1] -[2023-02-23 23:11:23,615][11103] Worker 3 uses CPU cores [1] -[2023-02-23 23:11:23,627][11106] Worker 6 uses CPU cores [0] -[2023-02-23 23:11:23,655][11101] Worker 1 uses CPU cores [1] -[2023-02-23 23:11:23,676][11104] Worker 4 uses CPU cores [0] -[2023-02-23 23:11:24,129][11099] Num visible devices: 1 -[2023-02-23 23:11:24,129][11085] Num visible devices: 1 -[2023-02-23 23:11:24,139][11085] Starting seed is not provided -[2023-02-23 23:11:24,140][11085] Using GPUs [0] for process 0 (actually maps to GPUs [0]) -[2023-02-23 23:11:24,141][11085] Initializing actor-critic model on device cuda:0 -[2023-02-23 23:11:24,142][11085] RunningMeanStd input shape: (3, 72, 128) -[2023-02-23 23:11:24,143][11085] RunningMeanStd input shape: (1,) -[2023-02-23 23:11:24,155][11085] ConvEncoder: input_channels=3 -[2023-02-23 23:11:24,406][11085] Conv encoder output size: 512 -[2023-02-23 23:11:24,406][11085] Policy head output size: 512 -[2023-02-23 23:11:24,451][11085] Created Actor Critic model with architecture: -[2023-02-23 23:11:24,451][11085] ActorCriticSharedWeights( +[2023-02-24 10:25:35,706][00397] Saving configuration to /content/train_dir/default_experiment/config.json... +[2023-02-24 10:25:35,708][00397] Rollout worker 0 uses device cpu +[2023-02-24 10:25:35,712][00397] Rollout worker 1 uses device cpu +[2023-02-24 10:25:35,714][00397] Rollout worker 2 uses device cpu +[2023-02-24 10:25:35,715][00397] Rollout worker 3 uses device cpu +[2023-02-24 10:25:35,716][00397] Rollout worker 4 uses device cpu +[2023-02-24 10:25:35,717][00397] Rollout worker 5 uses device cpu +[2023-02-24 10:25:35,719][00397] Rollout worker 6 uses device cpu +[2023-02-24 10:25:35,720][00397] Rollout worker 7 uses device cpu +[2023-02-24 10:25:35,906][00397] Using GPUs [0] for process 0 (actually maps to GPUs [0]) +[2023-02-24 10:25:35,908][00397] InferenceWorker_p0-w0: min num requests: 2 +[2023-02-24 10:25:35,938][00397] Starting all processes... +[2023-02-24 10:25:35,940][00397] Starting process learner_proc0 +[2023-02-24 10:25:35,999][00397] Starting all processes... +[2023-02-24 10:25:36,011][00397] Starting process inference_proc0-0 +[2023-02-24 10:25:36,011][00397] Starting process rollout_proc0 +[2023-02-24 10:25:36,015][00397] Starting process rollout_proc1 +[2023-02-24 10:25:36,016][00397] Starting process rollout_proc2 +[2023-02-24 10:25:36,016][00397] Starting process rollout_proc3 +[2023-02-24 10:25:36,016][00397] Starting process rollout_proc4 +[2023-02-24 10:25:36,016][00397] Starting process rollout_proc5 +[2023-02-24 10:25:36,016][00397] Starting process rollout_proc6 +[2023-02-24 10:25:36,016][00397] Starting process rollout_proc7 +[2023-02-24 10:25:47,806][12747] Using GPUs [0] for process 0 (actually maps to GPUs [0]) +[2023-02-24 10:25:47,811][12747] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0 +[2023-02-24 10:25:47,970][12764] Worker 1 uses CPU cores [1] +[2023-02-24 10:25:48,030][12767] Worker 5 uses CPU cores [1] +[2023-02-24 10:25:48,032][12763] Worker 2 uses CPU cores [0] +[2023-02-24 10:25:48,167][12769] Worker 6 uses CPU cores [0] +[2023-02-24 10:25:48,321][12761] Using GPUs [0] for process 0 (actually maps to GPUs [0]) +[2023-02-24 10:25:48,321][12761] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0 +[2023-02-24 10:25:48,355][12766] Worker 4 uses CPU cores [0] +[2023-02-24 10:25:48,360][12762] Worker 0 uses CPU cores [0] +[2023-02-24 10:25:48,427][12768] Worker 7 uses CPU cores [1] +[2023-02-24 10:25:48,430][12765] Worker 3 uses CPU cores [1] +[2023-02-24 10:25:48,686][12747] Num visible devices: 1 +[2023-02-24 10:25:48,686][12761] Num visible devices: 1 +[2023-02-24 10:25:48,701][12747] Starting seed is not provided +[2023-02-24 10:25:48,702][12747] Using GPUs [0] for process 0 (actually maps to GPUs [0]) +[2023-02-24 10:25:48,703][12747] Initializing actor-critic model on device cuda:0 +[2023-02-24 10:25:48,703][12747] RunningMeanStd input shape: (3, 72, 128) +[2023-02-24 10:25:48,705][12747] RunningMeanStd input shape: (1,) +[2023-02-24 10:25:48,717][12747] ConvEncoder: input_channels=3 +[2023-02-24 10:25:49,005][12747] Conv encoder output size: 512 +[2023-02-24 10:25:49,005][12747] Policy head output size: 512 +[2023-02-24 10:25:49,056][12747] Created Actor Critic model with architecture: +[2023-02-24 10:25:49,057][12747] ActorCriticSharedWeights( (obs_normalizer): ObservationNormalizer( (running_mean_std): RunningMeanStdDictInPlace( (running_mean_std): ModuleDict( @@ -85,1160 +85,1093 @@ (distribution_linear): Linear(in_features=512, out_features=5, bias=True) ) ) -[2023-02-23 23:11:30,548][11085] Using optimizer -[2023-02-23 23:11:30,550][11085] No checkpoints found -[2023-02-23 23:11:30,551][11085] Did not load from checkpoint, starting from scratch! -[2023-02-23 23:11:30,551][11085] Initialized policy 0 weights for model version 0 -[2023-02-23 23:11:30,554][11085] LearnerWorker_p0 finished initialization! -[2023-02-23 23:11:30,557][11085] Using GPUs [0] for process 0 (actually maps to GPUs [0]) -[2023-02-23 23:11:30,863][11099] RunningMeanStd input shape: (3, 72, 128) -[2023-02-23 23:11:30,865][11099] RunningMeanStd input shape: (1,) -[2023-02-23 23:11:30,883][11099] ConvEncoder: input_channels=3 -[2023-02-23 23:11:31,037][11099] Conv encoder output size: 512 -[2023-02-23 23:11:31,038][11099] Policy head output size: 512 -[2023-02-23 23:11:31,697][00448] Heartbeat connected on Batcher_0 -[2023-02-23 23:11:31,702][00448] Heartbeat connected on LearnerWorker_p0 -[2023-02-23 23:11:31,718][00448] Heartbeat connected on RolloutWorker_w0 -[2023-02-23 23:11:31,719][00448] Heartbeat connected on RolloutWorker_w1 -[2023-02-23 23:11:31,721][00448] Heartbeat connected on RolloutWorker_w2 -[2023-02-23 23:11:31,731][00448] Heartbeat connected on RolloutWorker_w3 -[2023-02-23 23:11:31,734][00448] Heartbeat connected on RolloutWorker_w4 -[2023-02-23 23:11:31,737][00448] Heartbeat connected on RolloutWorker_w5 -[2023-02-23 23:11:31,741][00448] Heartbeat connected on RolloutWorker_w6 -[2023-02-23 23:11:31,748][00448] Heartbeat connected on RolloutWorker_w7 -[2023-02-23 23:11:32,677][00448] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 0. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) -[2023-02-23 23:11:33,845][00448] Inference worker 0-0 is ready! -[2023-02-23 23:11:33,847][00448] All inference workers are ready! Signal rollout workers to start! -[2023-02-23 23:11:33,853][00448] Heartbeat connected on InferenceWorker_p0-w0 -[2023-02-23 23:11:33,965][11103] Doom resolution: 160x120, resize resolution: (128, 72) -[2023-02-23 23:11:33,976][11101] Doom resolution: 160x120, resize resolution: (128, 72) -[2023-02-23 23:11:33,977][11105] Doom resolution: 160x120, resize resolution: (128, 72) -[2023-02-23 23:11:33,978][11107] Doom resolution: 160x120, resize resolution: (128, 72) -[2023-02-23 23:11:34,004][11106] Doom resolution: 160x120, resize resolution: (128, 72) -[2023-02-23 23:11:34,014][11104] Doom resolution: 160x120, resize resolution: (128, 72) -[2023-02-23 23:11:34,023][11100] Doom resolution: 160x120, resize resolution: (128, 72) -[2023-02-23 23:11:34,038][11102] Doom resolution: 160x120, resize resolution: (128, 72) -[2023-02-23 23:11:34,189][11101] VizDoom game.init() threw an exception ViZDoomUnexpectedExitException('Controlled ViZDoom instance exited unexpectedly.'). Terminate process... -[2023-02-23 23:11:34,191][11103] VizDoom game.init() threw an exception ViZDoomUnexpectedExitException('Controlled ViZDoom instance exited unexpectedly.'). Terminate process... -[2023-02-23 23:11:34,194][11101] EvtLoop [rollout_proc1_evt_loop, process=rollout_proc1] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Sampler', signal_name='_inference_workers_initialized'), args=() -Traceback (most recent call last): - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 228, in _game_init - self.game.init() -vizdoom.vizdoom.ViZDoomUnexpectedExitException: Controlled ViZDoom instance exited unexpectedly. - -During handling of the above exception, another exception occurred: - -Traceback (most recent call last): - File "/usr/local/lib/python3.8/dist-packages/signal_slot/signal_slot.py", line 355, in _process_signal - slot_callable(*args) - File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/rollout_worker.py", line 150, in init - env_runner.init(self.timing) - File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 418, in init - self._reset() - File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 430, in _reset - observations, info = e.reset(seed=seed) # new way of doing seeding since Gym 0.26.0 - File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset - return self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 125, in reset - obs, info = self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 110, in reset - obs, info = self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 30, in reset - return self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 379, in reset - obs, info = self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sample_factory/envs/env_wrappers.py", line 84, in reset - obs, info = self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset - return self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 51, in reset - return self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 323, in reset - self._ensure_initialized() - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 274, in _ensure_initialized - self.initialize() - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 269, in initialize - self._game_init() - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 244, in _game_init - raise EnvCriticalError() -sample_factory.envs.env_utils.EnvCriticalError -[2023-02-23 23:11:34,196][11101] Unhandled exception in evt loop rollout_proc1_evt_loop -[2023-02-23 23:11:34,196][11103] EvtLoop [rollout_proc3_evt_loop, process=rollout_proc3] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Sampler', signal_name='_inference_workers_initialized'), args=() -Traceback (most recent call last): - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 228, in _game_init - self.game.init() -vizdoom.vizdoom.ViZDoomUnexpectedExitException: Controlled ViZDoom instance exited unexpectedly. - -During handling of the above exception, another exception occurred: - -Traceback (most recent call last): - File "/usr/local/lib/python3.8/dist-packages/signal_slot/signal_slot.py", line 355, in _process_signal - slot_callable(*args) - File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/rollout_worker.py", line 150, in init - env_runner.init(self.timing) - File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 418, in init - self._reset() - File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 430, in _reset - observations, info = e.reset(seed=seed) # new way of doing seeding since Gym 0.26.0 - File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset - return self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 125, in reset - obs, info = self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 110, in reset - obs, info = self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 30, in reset - return self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 379, in reset - obs, info = self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sample_factory/envs/env_wrappers.py", line 84, in reset - obs, info = self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset - return self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 51, in reset - return self.env.reset(**kwargs) - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 323, in reset - self._ensure_initialized() - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 274, in _ensure_initialized - self.initialize() - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 269, in initialize - self._game_init() - File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 244, in _game_init - raise EnvCriticalError() -sample_factory.envs.env_utils.EnvCriticalError -[2023-02-23 23:11:34,200][11103] Unhandled exception in evt loop rollout_proc3_evt_loop -[2023-02-23 23:11:35,448][11107] Decorrelating experience for 0 frames... -[2023-02-23 23:11:35,500][11100] Decorrelating experience for 0 frames... -[2023-02-23 23:11:35,507][11106] Decorrelating experience for 0 frames... -[2023-02-23 23:11:35,513][11104] Decorrelating experience for 0 frames... -[2023-02-23 23:11:35,516][11102] Decorrelating experience for 0 frames... -[2023-02-23 23:11:35,576][11105] Decorrelating experience for 0 frames... -[2023-02-23 23:11:35,867][11107] Decorrelating experience for 32 frames... -[2023-02-23 23:11:36,588][11105] Decorrelating experience for 32 frames... -[2023-02-23 23:11:36,673][11107] Decorrelating experience for 64 frames... -[2023-02-23 23:11:36,824][11102] Decorrelating experience for 32 frames... -[2023-02-23 23:11:36,834][11104] Decorrelating experience for 32 frames... -[2023-02-23 23:11:36,837][11106] Decorrelating experience for 32 frames... -[2023-02-23 23:11:37,011][11100] Decorrelating experience for 32 frames... -[2023-02-23 23:11:37,309][11105] Decorrelating experience for 64 frames... -[2023-02-23 23:11:37,560][11107] Decorrelating experience for 96 frames... -[2023-02-23 23:11:37,677][00448] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 0.0. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) -[2023-02-23 23:11:38,045][11105] Decorrelating experience for 96 frames... -[2023-02-23 23:11:38,384][11104] Decorrelating experience for 64 frames... -[2023-02-23 23:11:38,386][11102] Decorrelating experience for 64 frames... -[2023-02-23 23:11:38,401][11106] Decorrelating experience for 64 frames... -[2023-02-23 23:11:38,687][11100] Decorrelating experience for 64 frames... -[2023-02-23 23:11:39,428][11102] Decorrelating experience for 96 frames... -[2023-02-23 23:11:39,439][11104] Decorrelating experience for 96 frames... -[2023-02-23 23:11:39,457][11106] Decorrelating experience for 96 frames... -[2023-02-23 23:11:39,943][11100] Decorrelating experience for 96 frames... -[2023-02-23 23:11:42,677][00448] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 2.8. Samples: 28. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) -[2023-02-23 23:11:42,683][00448] Avg episode reward: [(0, '1.925')] -[2023-02-23 23:11:43,758][11085] Signal inference workers to stop experience collection... -[2023-02-23 23:11:43,784][11099] InferenceWorker_p0-w0: stopping experience collection -[2023-02-23 23:11:46,367][11085] Signal inference workers to resume experience collection... -[2023-02-23 23:11:46,367][11099] InferenceWorker_p0-w0: resuming experience collection -[2023-02-23 23:11:47,678][00448] Fps is (10 sec: 409.5, 60 sec: 273.0, 300 sec: 273.0). Total num frames: 4096. Throughput: 0: 156.4. Samples: 2346. Policy #0 lag: (min: 0.0, avg: 0.0, max: 0.0) -[2023-02-23 23:11:47,692][00448] Avg episode reward: [(0, '3.084')] -[2023-02-23 23:11:52,677][00448] Fps is (10 sec: 2457.6, 60 sec: 1228.8, 300 sec: 1228.8). Total num frames: 24576. Throughput: 0: 320.7. Samples: 6414. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:11:52,683][00448] Avg episode reward: [(0, '3.815')] -[2023-02-23 23:11:55,644][11099] Updated weights for policy 0, policy_version 10 (0.0021) -[2023-02-23 23:11:57,677][00448] Fps is (10 sec: 4506.3, 60 sec: 1966.1, 300 sec: 1966.1). Total num frames: 49152. Throughput: 0: 393.1. Samples: 9828. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:11:57,679][00448] Avg episode reward: [(0, '4.233')] -[2023-02-23 23:12:02,679][00448] Fps is (10 sec: 4095.3, 60 sec: 2184.4, 300 sec: 2184.4). Total num frames: 65536. Throughput: 0: 538.4. Samples: 16154. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:12:02,686][00448] Avg episode reward: [(0, '4.249')] -[2023-02-23 23:12:07,306][11099] Updated weights for policy 0, policy_version 20 (0.0011) -[2023-02-23 23:12:07,677][00448] Fps is (10 sec: 3276.8, 60 sec: 2340.6, 300 sec: 2340.6). Total num frames: 81920. Throughput: 0: 590.9. Samples: 20682. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:12:07,684][00448] Avg episode reward: [(0, '4.251')] -[2023-02-23 23:12:12,677][00448] Fps is (10 sec: 3687.1, 60 sec: 2560.0, 300 sec: 2560.0). Total num frames: 102400. Throughput: 0: 592.2. Samples: 23686. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:12:12,682][00448] Avg episode reward: [(0, '4.259')] -[2023-02-23 23:12:12,685][11085] Saving new best policy, reward=4.259! -[2023-02-23 23:12:16,394][11099] Updated weights for policy 0, policy_version 30 (0.0013) -[2023-02-23 23:12:17,677][00448] Fps is (10 sec: 4505.6, 60 sec: 2821.7, 300 sec: 2821.7). Total num frames: 126976. Throughput: 0: 673.1. Samples: 30288. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:12:17,679][00448] Avg episode reward: [(0, '4.291')] -[2023-02-23 23:12:17,693][11085] Saving new best policy, reward=4.291! -[2023-02-23 23:12:22,677][00448] Fps is (10 sec: 3686.4, 60 sec: 2785.3, 300 sec: 2785.3). Total num frames: 139264. Throughput: 0: 788.3. Samples: 35474. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:12:22,679][00448] Avg episode reward: [(0, '4.267')] -[2023-02-23 23:12:27,677][00448] Fps is (10 sec: 3276.7, 60 sec: 2904.4, 300 sec: 2904.4). Total num frames: 159744. Throughput: 0: 839.7. Samples: 37816. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:12:27,680][00448] Avg episode reward: [(0, '4.349')] -[2023-02-23 23:12:27,691][11085] Saving new best policy, reward=4.349! -[2023-02-23 23:12:28,412][11099] Updated weights for policy 0, policy_version 40 (0.0018) -[2023-02-23 23:12:32,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3003.7, 300 sec: 3003.7). Total num frames: 180224. Throughput: 0: 925.8. Samples: 44004. Policy #0 lag: (min: 0.0, avg: 0.3, max: 2.0) -[2023-02-23 23:12:32,679][00448] Avg episode reward: [(0, '4.344')] -[2023-02-23 23:12:37,681][00448] Fps is (10 sec: 4094.3, 60 sec: 3344.8, 300 sec: 3087.5). Total num frames: 200704. Throughput: 0: 981.0. Samples: 50562. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:12:37,686][00448] Avg episode reward: [(0, '4.451')] -[2023-02-23 23:12:37,695][11085] Saving new best policy, reward=4.451! -[2023-02-23 23:12:38,129][11099] Updated weights for policy 0, policy_version 50 (0.0011) -[2023-02-23 23:12:42,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3618.1, 300 sec: 3101.3). Total num frames: 217088. Throughput: 0: 953.6. Samples: 52742. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:12:42,683][00448] Avg episode reward: [(0, '4.515')] -[2023-02-23 23:12:42,687][11085] Saving new best policy, reward=4.515! -[2023-02-23 23:12:47,677][00448] Fps is (10 sec: 3278.3, 60 sec: 3823.0, 300 sec: 3113.0). Total num frames: 233472. Throughput: 0: 924.0. Samples: 57732. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:12:47,679][00448] Avg episode reward: [(0, '4.455')] -[2023-02-23 23:12:49,438][11099] Updated weights for policy 0, policy_version 60 (0.0020) -[2023-02-23 23:12:52,677][00448] Fps is (10 sec: 4095.9, 60 sec: 3891.2, 300 sec: 3225.6). Total num frames: 258048. Throughput: 0: 968.5. Samples: 64264. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:12:52,679][00448] Avg episode reward: [(0, '4.463')] -[2023-02-23 23:12:57,680][00448] Fps is (10 sec: 4094.6, 60 sec: 3754.5, 300 sec: 3228.5). Total num frames: 274432. Throughput: 0: 971.1. Samples: 67390. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:12:57,684][00448] Avg episode reward: [(0, '4.680')] -[2023-02-23 23:12:57,697][11085] Saving new best policy, reward=4.680! -[2023-02-23 23:13:01,770][11099] Updated weights for policy 0, policy_version 70 (0.0025) -[2023-02-23 23:13:02,677][00448] Fps is (10 sec: 2867.2, 60 sec: 3686.5, 300 sec: 3185.8). Total num frames: 286720. Throughput: 0: 915.7. Samples: 71494. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:13:02,683][00448] Avg episode reward: [(0, '4.493')] -[2023-02-23 23:13:07,677][00448] Fps is (10 sec: 3277.9, 60 sec: 3754.7, 300 sec: 3233.7). Total num frames: 307200. Throughput: 0: 929.4. Samples: 77298. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:13:07,679][00448] Avg episode reward: [(0, '4.532')] -[2023-02-23 23:13:07,689][11085] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000075_307200.pth... -[2023-02-23 23:13:11,453][11099] Updated weights for policy 0, policy_version 80 (0.0022) -[2023-02-23 23:13:12,677][00448] Fps is (10 sec: 4505.7, 60 sec: 3822.9, 300 sec: 3317.8). Total num frames: 331776. Throughput: 0: 950.5. Samples: 80588. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:13:12,681][00448] Avg episode reward: [(0, '4.363')] -[2023-02-23 23:13:17,678][00448] Fps is (10 sec: 4095.4, 60 sec: 3686.3, 300 sec: 3315.8). Total num frames: 348160. Throughput: 0: 938.1. Samples: 86220. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:13:17,682][00448] Avg episode reward: [(0, '4.448')] -[2023-02-23 23:13:22,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3314.0). Total num frames: 364544. Throughput: 0: 896.0. Samples: 90878. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:13:22,679][00448] Avg episode reward: [(0, '4.506')] -[2023-02-23 23:13:23,288][11099] Updated weights for policy 0, policy_version 90 (0.0011) -[2023-02-23 23:13:27,677][00448] Fps is (10 sec: 3686.9, 60 sec: 3754.7, 300 sec: 3348.0). Total num frames: 385024. Throughput: 0: 923.5. Samples: 94298. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:13:27,679][00448] Avg episode reward: [(0, '4.544')] -[2023-02-23 23:13:32,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3379.2). Total num frames: 405504. Throughput: 0: 960.1. Samples: 100936. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:13:32,682][00448] Avg episode reward: [(0, '4.714')] -[2023-02-23 23:13:32,686][11085] Saving new best policy, reward=4.714! -[2023-02-23 23:13:33,141][11099] Updated weights for policy 0, policy_version 100 (0.0012) -[2023-02-23 23:13:37,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3686.7, 300 sec: 3375.1). Total num frames: 421888. Throughput: 0: 916.7. Samples: 105516. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:13:37,680][00448] Avg episode reward: [(0, '4.559')] -[2023-02-23 23:13:42,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3402.8). Total num frames: 442368. Throughput: 0: 900.1. Samples: 107890. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:13:42,680][00448] Avg episode reward: [(0, '4.236')] -[2023-02-23 23:13:44,529][11099] Updated weights for policy 0, policy_version 110 (0.0029) -[2023-02-23 23:13:47,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3428.5). Total num frames: 462848. Throughput: 0: 958.1. Samples: 114610. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:13:47,681][00448] Avg episode reward: [(0, '4.286')] -[2023-02-23 23:13:52,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3452.3). Total num frames: 483328. Throughput: 0: 961.5. Samples: 120566. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:13:52,681][00448] Avg episode reward: [(0, '4.508')] -[2023-02-23 23:13:55,564][11099] Updated weights for policy 0, policy_version 120 (0.0011) -[2023-02-23 23:13:57,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.6, 300 sec: 3418.0). Total num frames: 495616. Throughput: 0: 937.8. Samples: 122790. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:13:57,682][00448] Avg episode reward: [(0, '4.560')] -[2023-02-23 23:14:02,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3823.0, 300 sec: 3440.6). Total num frames: 516096. Throughput: 0: 936.4. Samples: 128356. Policy #0 lag: (min: 0.0, avg: 0.3, max: 2.0) -[2023-02-23 23:14:02,685][00448] Avg episode reward: [(0, '4.470')] -[2023-02-23 23:14:05,493][11099] Updated weights for policy 0, policy_version 130 (0.0018) -[2023-02-23 23:14:07,677][00448] Fps is (10 sec: 4505.6, 60 sec: 3891.2, 300 sec: 3488.2). Total num frames: 540672. Throughput: 0: 982.8. Samples: 135102. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:14:07,681][00448] Avg episode reward: [(0, '4.538')] -[2023-02-23 23:14:12,677][00448] Fps is (10 sec: 4095.9, 60 sec: 3754.7, 300 sec: 3481.6). Total num frames: 557056. Throughput: 0: 964.1. Samples: 137682. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:14:12,683][00448] Avg episode reward: [(0, '4.715')] -[2023-02-23 23:14:17,497][11099] Updated weights for policy 0, policy_version 140 (0.0018) -[2023-02-23 23:14:17,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3754.8, 300 sec: 3475.4). Total num frames: 573440. Throughput: 0: 916.6. Samples: 142184. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:14:17,680][00448] Avg episode reward: [(0, '4.632')] -[2023-02-23 23:14:22,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3493.6). Total num frames: 593920. Throughput: 0: 957.3. Samples: 148596. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:14:22,679][00448] Avg episode reward: [(0, '4.468')] -[2023-02-23 23:14:26,805][11099] Updated weights for policy 0, policy_version 150 (0.0017) -[2023-02-23 23:14:27,677][00448] Fps is (10 sec: 4095.9, 60 sec: 3822.9, 300 sec: 3510.9). Total num frames: 614400. Throughput: 0: 977.6. Samples: 151882. Policy #0 lag: (min: 0.0, avg: 0.3, max: 2.0) -[2023-02-23 23:14:27,679][00448] Avg episode reward: [(0, '4.415')] -[2023-02-23 23:14:32,682][00448] Fps is (10 sec: 3684.4, 60 sec: 3754.3, 300 sec: 3504.2). Total num frames: 630784. Throughput: 0: 943.5. Samples: 157074. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:14:32,686][00448] Avg episode reward: [(0, '4.442')] -[2023-02-23 23:14:37,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3498.2). Total num frames: 647168. Throughput: 0: 926.2. Samples: 162246. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:14:37,679][00448] Avg episode reward: [(0, '4.585')] -[2023-02-23 23:14:38,617][11099] Updated weights for policy 0, policy_version 160 (0.0013) -[2023-02-23 23:14:42,677][00448] Fps is (10 sec: 4098.2, 60 sec: 3822.9, 300 sec: 3535.5). Total num frames: 671744. Throughput: 0: 952.7. Samples: 165660. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:14:42,679][00448] Avg episode reward: [(0, '4.399')] -[2023-02-23 23:14:47,679][00448] Fps is (10 sec: 4095.0, 60 sec: 3754.5, 300 sec: 3528.8). Total num frames: 688128. Throughput: 0: 970.7. Samples: 172040. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:14:47,682][00448] Avg episode reward: [(0, '4.405')] -[2023-02-23 23:14:49,048][11099] Updated weights for policy 0, policy_version 170 (0.0011) -[2023-02-23 23:14:52,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3522.6). Total num frames: 704512. Throughput: 0: 921.4. Samples: 176564. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:14:52,680][00448] Avg episode reward: [(0, '4.456')] -[2023-02-23 23:14:57,677][00448] Fps is (10 sec: 3277.6, 60 sec: 3754.7, 300 sec: 3516.6). Total num frames: 720896. Throughput: 0: 908.4. Samples: 178560. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:14:57,679][00448] Avg episode reward: [(0, '4.428')] -[2023-02-23 23:15:02,373][11099] Updated weights for policy 0, policy_version 180 (0.0016) -[2023-02-23 23:15:02,677][00448] Fps is (10 sec: 3276.7, 60 sec: 3686.4, 300 sec: 3510.9). Total num frames: 737280. Throughput: 0: 910.2. Samples: 183142. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:15:02,680][00448] Avg episode reward: [(0, '4.797')] -[2023-02-23 23:15:02,685][11085] Saving new best policy, reward=4.797! -[2023-02-23 23:15:07,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3505.4). Total num frames: 753664. Throughput: 0: 891.8. Samples: 188726. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:15:07,684][00448] Avg episode reward: [(0, '4.619')] -[2023-02-23 23:15:07,703][11085] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000184_753664.pth... -[2023-02-23 23:15:12,677][00448] Fps is (10 sec: 2867.3, 60 sec: 3481.6, 300 sec: 3481.6). Total num frames: 765952. Throughput: 0: 861.2. Samples: 190634. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:15:12,680][00448] Avg episode reward: [(0, '4.517')] -[2023-02-23 23:15:14,699][11099] Updated weights for policy 0, policy_version 190 (0.0014) -[2023-02-23 23:15:17,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3618.1, 300 sec: 3513.5). Total num frames: 790528. Throughput: 0: 868.7. Samples: 196160. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:15:17,679][00448] Avg episode reward: [(0, '4.528')] -[2023-02-23 23:15:22,677][00448] Fps is (10 sec: 4505.5, 60 sec: 3618.1, 300 sec: 3526.1). Total num frames: 811008. Throughput: 0: 904.0. Samples: 202928. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:15:22,679][00448] Avg episode reward: [(0, '4.620')] -[2023-02-23 23:15:24,432][11099] Updated weights for policy 0, policy_version 200 (0.0015) -[2023-02-23 23:15:27,682][00448] Fps is (10 sec: 3684.6, 60 sec: 3549.6, 300 sec: 3520.7). Total num frames: 827392. Throughput: 0: 886.4. Samples: 205554. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:15:27,684][00448] Avg episode reward: [(0, '4.591')] -[2023-02-23 23:15:32,677][00448] Fps is (10 sec: 3276.9, 60 sec: 3550.2, 300 sec: 3515.7). Total num frames: 843776. Throughput: 0: 845.3. Samples: 210076. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:15:32,680][00448] Avg episode reward: [(0, '4.785')] -[2023-02-23 23:15:36,007][11099] Updated weights for policy 0, policy_version 210 (0.0020) -[2023-02-23 23:15:37,677][00448] Fps is (10 sec: 3688.1, 60 sec: 3618.1, 300 sec: 3527.6). Total num frames: 864256. Throughput: 0: 884.9. Samples: 216384. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:15:37,687][00448] Avg episode reward: [(0, '4.794')] -[2023-02-23 23:15:42,677][00448] Fps is (10 sec: 4096.1, 60 sec: 3549.9, 300 sec: 3538.9). Total num frames: 884736. Throughput: 0: 914.3. Samples: 219704. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:15:42,681][00448] Avg episode reward: [(0, '4.697')] -[2023-02-23 23:15:47,177][11099] Updated weights for policy 0, policy_version 220 (0.0011) -[2023-02-23 23:15:47,679][00448] Fps is (10 sec: 3685.7, 60 sec: 3549.9, 300 sec: 3533.8). Total num frames: 901120. Throughput: 0: 923.9. Samples: 224718. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:15:47,684][00448] Avg episode reward: [(0, '4.593')] -[2023-02-23 23:15:52,678][00448] Fps is (10 sec: 3276.3, 60 sec: 3549.8, 300 sec: 3528.8). Total num frames: 917504. Throughput: 0: 911.2. Samples: 229730. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:15:52,681][00448] Avg episode reward: [(0, '4.477')] -[2023-02-23 23:15:57,677][00448] Fps is (10 sec: 3687.1, 60 sec: 3618.1, 300 sec: 3539.6). Total num frames: 937984. Throughput: 0: 936.2. Samples: 232762. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:15:57,683][00448] Avg episode reward: [(0, '4.801')] -[2023-02-23 23:15:57,694][11085] Saving new best policy, reward=4.801! -[2023-02-23 23:15:57,947][11099] Updated weights for policy 0, policy_version 230 (0.0015) -[2023-02-23 23:16:02,677][00448] Fps is (10 sec: 4096.6, 60 sec: 3686.4, 300 sec: 3549.9). Total num frames: 958464. Throughput: 0: 951.2. Samples: 238964. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:16:02,685][00448] Avg episode reward: [(0, '4.937')] -[2023-02-23 23:16:02,687][11085] Saving new best policy, reward=4.937! -[2023-02-23 23:16:07,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3618.1, 300 sec: 3530.0). Total num frames: 970752. Throughput: 0: 893.6. Samples: 243138. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:16:07,683][00448] Avg episode reward: [(0, '4.733')] -[2023-02-23 23:16:10,366][11099] Updated weights for policy 0, policy_version 240 (0.0022) -[2023-02-23 23:16:12,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3540.1). Total num frames: 991232. Throughput: 0: 890.2. Samples: 245610. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:16:12,679][00448] Avg episode reward: [(0, '4.692')] -[2023-02-23 23:16:17,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3549.9). Total num frames: 1011712. Throughput: 0: 926.7. Samples: 251778. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:16:17,684][00448] Avg episode reward: [(0, '4.772')] -[2023-02-23 23:16:20,867][11099] Updated weights for policy 0, policy_version 250 (0.0017) -[2023-02-23 23:16:22,677][00448] Fps is (10 sec: 3686.3, 60 sec: 3618.1, 300 sec: 3545.2). Total num frames: 1028096. Throughput: 0: 904.7. Samples: 257096. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:16:22,680][00448] Avg episode reward: [(0, '4.731')] -[2023-02-23 23:16:27,677][00448] Fps is (10 sec: 2867.2, 60 sec: 3550.1, 300 sec: 3526.7). Total num frames: 1040384. Throughput: 0: 877.6. Samples: 259196. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:16:27,684][00448] Avg episode reward: [(0, '4.655')] -[2023-02-23 23:16:32,500][11099] Updated weights for policy 0, policy_version 260 (0.0018) -[2023-02-23 23:16:32,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3610.0). Total num frames: 1064960. Throughput: 0: 890.1. Samples: 264772. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:16:32,680][00448] Avg episode reward: [(0, '4.785')] -[2023-02-23 23:16:37,679][00448] Fps is (10 sec: 4504.8, 60 sec: 3686.3, 300 sec: 3679.4). Total num frames: 1085440. Throughput: 0: 929.1. Samples: 271540. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:16:37,682][00448] Avg episode reward: [(0, '4.703')] -[2023-02-23 23:16:42,679][00448] Fps is (10 sec: 3685.8, 60 sec: 3618.0, 300 sec: 3721.1). Total num frames: 1101824. Throughput: 0: 913.9. Samples: 273890. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:16:42,681][00448] Avg episode reward: [(0, '4.616')] -[2023-02-23 23:16:43,798][11099] Updated weights for policy 0, policy_version 270 (0.0011) -[2023-02-23 23:16:47,677][00448] Fps is (10 sec: 3277.4, 60 sec: 3618.3, 300 sec: 3707.2). Total num frames: 1118208. Throughput: 0: 878.9. Samples: 278516. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:16:47,679][00448] Avg episode reward: [(0, '4.475')] -[2023-02-23 23:16:52,677][00448] Fps is (10 sec: 3687.1, 60 sec: 3686.5, 300 sec: 3693.3). Total num frames: 1138688. Throughput: 0: 931.6. Samples: 285062. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:16:52,679][00448] Avg episode reward: [(0, '4.409')] -[2023-02-23 23:16:53,693][11099] Updated weights for policy 0, policy_version 280 (0.0015) -[2023-02-23 23:16:57,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3707.2). Total num frames: 1159168. Throughput: 0: 951.3. Samples: 288420. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:16:57,681][00448] Avg episode reward: [(0, '4.623')] -[2023-02-23 23:17:02,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3618.1, 300 sec: 3707.2). Total num frames: 1175552. Throughput: 0: 921.0. Samples: 293224. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:17:02,680][00448] Avg episode reward: [(0, '4.586')] -[2023-02-23 23:17:05,658][11099] Updated weights for policy 0, policy_version 290 (0.0030) -[2023-02-23 23:17:07,680][00448] Fps is (10 sec: 3685.4, 60 sec: 3754.5, 300 sec: 3707.2). Total num frames: 1196032. Throughput: 0: 922.8. Samples: 298626. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) -[2023-02-23 23:17:07,683][00448] Avg episode reward: [(0, '4.463')] -[2023-02-23 23:17:07,696][11085] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000292_1196032.pth... -[2023-02-23 23:17:07,824][11085] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000075_307200.pth -[2023-02-23 23:17:12,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3693.3). Total num frames: 1216512. Throughput: 0: 948.1. Samples: 301860. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:17:12,680][00448] Avg episode reward: [(0, '4.702')] -[2023-02-23 23:17:15,499][11099] Updated weights for policy 0, policy_version 300 (0.0012) -[2023-02-23 23:17:17,677][00448] Fps is (10 sec: 3687.4, 60 sec: 3686.4, 300 sec: 3707.2). Total num frames: 1232896. Throughput: 0: 960.8. Samples: 308008. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:17:17,679][00448] Avg episode reward: [(0, '4.788')] -[2023-02-23 23:17:22,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3693.3). Total num frames: 1249280. Throughput: 0: 911.3. Samples: 312546. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:17:22,680][00448] Avg episode reward: [(0, '4.931')] -[2023-02-23 23:17:27,016][11099] Updated weights for policy 0, policy_version 310 (0.0018) -[2023-02-23 23:17:27,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3693.3). Total num frames: 1269760. Throughput: 0: 924.2. Samples: 315478. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:17:27,681][00448] Avg episode reward: [(0, '4.755')] -[2023-02-23 23:17:32,677][00448] Fps is (10 sec: 4505.6, 60 sec: 3823.0, 300 sec: 3707.3). Total num frames: 1294336. Throughput: 0: 971.4. Samples: 322228. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:17:32,686][00448] Avg episode reward: [(0, '4.561')] -[2023-02-23 23:17:37,642][11099] Updated weights for policy 0, policy_version 320 (0.0011) -[2023-02-23 23:17:37,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3754.8, 300 sec: 3707.2). Total num frames: 1310720. Throughput: 0: 943.6. Samples: 327526. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:17:37,684][00448] Avg episode reward: [(0, '4.697')] -[2023-02-23 23:17:42,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3754.8, 300 sec: 3707.2). Total num frames: 1327104. Throughput: 0: 919.5. Samples: 329798. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:17:42,680][00448] Avg episode reward: [(0, '4.905')] -[2023-02-23 23:17:47,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3693.3). Total num frames: 1347584. Throughput: 0: 951.3. Samples: 336034. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:17:47,680][00448] Avg episode reward: [(0, '4.890')] -[2023-02-23 23:17:48,028][11099] Updated weights for policy 0, policy_version 330 (0.0017) -[2023-02-23 23:17:52,677][00448] Fps is (10 sec: 4095.9, 60 sec: 3822.9, 300 sec: 3707.3). Total num frames: 1368064. Throughput: 0: 979.7. Samples: 342710. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:17:52,682][00448] Avg episode reward: [(0, '4.798')] -[2023-02-23 23:17:57,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3721.1). Total num frames: 1384448. Throughput: 0: 956.2. Samples: 344888. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:17:57,681][00448] Avg episode reward: [(0, '4.902')] -[2023-02-23 23:17:59,577][11099] Updated weights for policy 0, policy_version 340 (0.0012) -[2023-02-23 23:18:02,677][00448] Fps is (10 sec: 3686.5, 60 sec: 3822.9, 300 sec: 3721.1). Total num frames: 1404928. Throughput: 0: 926.5. Samples: 349702. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:18:02,679][00448] Avg episode reward: [(0, '4.859')] -[2023-02-23 23:18:07,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3823.1, 300 sec: 3707.2). Total num frames: 1425408. Throughput: 0: 967.7. Samples: 356094. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:18:07,680][00448] Avg episode reward: [(0, '4.810')] -[2023-02-23 23:18:09,572][11099] Updated weights for policy 0, policy_version 350 (0.0012) -[2023-02-23 23:18:12,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 1441792. Throughput: 0: 970.5. Samples: 359150. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:18:12,683][00448] Avg episode reward: [(0, '5.002')] -[2023-02-23 23:18:12,691][11085] Saving new best policy, reward=5.002! -[2023-02-23 23:18:17,679][00448] Fps is (10 sec: 2866.7, 60 sec: 3686.3, 300 sec: 3693.3). Total num frames: 1454080. Throughput: 0: 913.6. Samples: 363340. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:18:17,683][00448] Avg episode reward: [(0, '5.010')] -[2023-02-23 23:18:17,696][11085] Saving new best policy, reward=5.010! -[2023-02-23 23:18:22,001][11099] Updated weights for policy 0, policy_version 360 (0.0018) -[2023-02-23 23:18:22,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3693.3). Total num frames: 1474560. Throughput: 0: 920.4. Samples: 368946. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:18:22,683][00448] Avg episode reward: [(0, '5.002')] -[2023-02-23 23:18:27,677][00448] Fps is (10 sec: 4506.4, 60 sec: 3822.9, 300 sec: 3707.2). Total num frames: 1499136. Throughput: 0: 941.0. Samples: 372144. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:18:27,680][00448] Avg episode reward: [(0, '5.324')] -[2023-02-23 23:18:27,693][11085] Saving new best policy, reward=5.324! -[2023-02-23 23:18:32,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3618.1, 300 sec: 3693.3). Total num frames: 1511424. Throughput: 0: 929.0. Samples: 377838. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:18:32,682][00448] Avg episode reward: [(0, '5.429')] -[2023-02-23 23:18:32,692][11085] Saving new best policy, reward=5.429! -[2023-02-23 23:18:32,954][11099] Updated weights for policy 0, policy_version 370 (0.0015) -[2023-02-23 23:18:37,679][00448] Fps is (10 sec: 2866.6, 60 sec: 3618.0, 300 sec: 3679.4). Total num frames: 1527808. Throughput: 0: 877.7. Samples: 382208. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:18:37,681][00448] Avg episode reward: [(0, '5.533')] -[2023-02-23 23:18:37,697][11085] Saving new best policy, reward=5.533! -[2023-02-23 23:18:42,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3693.3). Total num frames: 1552384. Throughput: 0: 901.2. Samples: 385444. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:18:42,682][00448] Avg episode reward: [(0, '5.251')] -[2023-02-23 23:18:43,872][11099] Updated weights for policy 0, policy_version 380 (0.0017) -[2023-02-23 23:18:47,678][00448] Fps is (10 sec: 3686.7, 60 sec: 3618.1, 300 sec: 3665.6). Total num frames: 1564672. Throughput: 0: 917.5. Samples: 390990. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:18:47,681][00448] Avg episode reward: [(0, '5.199')] -[2023-02-23 23:18:52,677][00448] Fps is (10 sec: 2457.6, 60 sec: 3481.6, 300 sec: 3665.6). Total num frames: 1576960. Throughput: 0: 858.8. Samples: 394742. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:18:52,681][00448] Avg episode reward: [(0, '5.209')] -[2023-02-23 23:18:57,677][00448] Fps is (10 sec: 2867.5, 60 sec: 3481.6, 300 sec: 3651.7). Total num frames: 1593344. Throughput: 0: 834.1. Samples: 396686. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:18:57,682][00448] Avg episode reward: [(0, '5.233')] -[2023-02-23 23:18:58,378][11099] Updated weights for policy 0, policy_version 390 (0.0019) -[2023-02-23 23:19:02,677][00448] Fps is (10 sec: 3686.3, 60 sec: 3481.6, 300 sec: 3637.8). Total num frames: 1613824. Throughput: 0: 870.5. Samples: 402510. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:19:02,682][00448] Avg episode reward: [(0, '5.237')] -[2023-02-23 23:19:07,569][11099] Updated weights for policy 0, policy_version 400 (0.0011) -[2023-02-23 23:19:07,677][00448] Fps is (10 sec: 4505.6, 60 sec: 3549.9, 300 sec: 3665.6). Total num frames: 1638400. Throughput: 0: 893.6. Samples: 409158. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:19:07,683][00448] Avg episode reward: [(0, '5.583')] -[2023-02-23 23:19:07,695][11085] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000400_1638400.pth... -[2023-02-23 23:19:07,831][11085] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000184_753664.pth -[2023-02-23 23:19:07,843][11085] Saving new best policy, reward=5.583! -[2023-02-23 23:19:12,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3651.7). Total num frames: 1650688. Throughput: 0: 874.1. Samples: 411478. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:19:12,682][00448] Avg episode reward: [(0, '5.710')] -[2023-02-23 23:19:12,687][11085] Saving new best policy, reward=5.710! -[2023-02-23 23:19:17,677][00448] Fps is (10 sec: 2867.2, 60 sec: 3550.0, 300 sec: 3637.8). Total num frames: 1667072. Throughput: 0: 841.7. Samples: 415714. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:19:17,680][00448] Avg episode reward: [(0, '5.759')] -[2023-02-23 23:19:17,695][11085] Saving new best policy, reward=5.759! -[2023-02-23 23:19:20,109][11099] Updated weights for policy 0, policy_version 410 (0.0014) -[2023-02-23 23:19:22,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3637.8). Total num frames: 1687552. Throughput: 0: 890.0. Samples: 422258. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:19:22,680][00448] Avg episode reward: [(0, '6.286')] -[2023-02-23 23:19:22,683][11085] Saving new best policy, reward=6.286! -[2023-02-23 23:19:27,680][00448] Fps is (10 sec: 4094.6, 60 sec: 3481.4, 300 sec: 3651.7). Total num frames: 1708032. Throughput: 0: 890.4. Samples: 425514. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:19:27,682][00448] Avg episode reward: [(0, '6.477')] -[2023-02-23 23:19:27,699][11085] Saving new best policy, reward=6.477! -[2023-02-23 23:19:31,390][11099] Updated weights for policy 0, policy_version 420 (0.0011) -[2023-02-23 23:19:32,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3637.8). Total num frames: 1720320. Throughput: 0: 868.4. Samples: 430066. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:19:32,683][00448] Avg episode reward: [(0, '6.540')] -[2023-02-23 23:19:32,712][11085] Saving new best policy, reward=6.540! -[2023-02-23 23:19:37,677][00448] Fps is (10 sec: 3278.0, 60 sec: 3550.0, 300 sec: 3623.9). Total num frames: 1740800. Throughput: 0: 900.5. Samples: 435266. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:19:37,680][00448] Avg episode reward: [(0, '6.306')] -[2023-02-23 23:19:42,083][11099] Updated weights for policy 0, policy_version 430 (0.0015) -[2023-02-23 23:19:42,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3481.6, 300 sec: 3637.8). Total num frames: 1761280. Throughput: 0: 927.8. Samples: 438436. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:19:42,682][00448] Avg episode reward: [(0, '6.460')] -[2023-02-23 23:19:47,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3637.8). Total num frames: 1777664. Throughput: 0: 930.8. Samples: 444398. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:19:47,681][00448] Avg episode reward: [(0, '6.551')] -[2023-02-23 23:19:47,695][11085] Saving new best policy, reward=6.551! -[2023-02-23 23:19:52,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3618.1, 300 sec: 3637.8). Total num frames: 1794048. Throughput: 0: 876.4. Samples: 448594. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:19:52,682][00448] Avg episode reward: [(0, '6.445')] -[2023-02-23 23:19:54,441][11099] Updated weights for policy 0, policy_version 440 (0.0014) -[2023-02-23 23:19:57,678][00448] Fps is (10 sec: 3686.0, 60 sec: 3686.3, 300 sec: 3651.7). Total num frames: 1814528. Throughput: 0: 887.0. Samples: 451392. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:19:57,683][00448] Avg episode reward: [(0, '7.097')] -[2023-02-23 23:19:57,694][11085] Saving new best policy, reward=7.097! -[2023-02-23 23:20:02,679][00448] Fps is (10 sec: 4095.1, 60 sec: 3686.3, 300 sec: 3665.5). Total num frames: 1835008. Throughput: 0: 938.8. Samples: 457962. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:20:02,685][00448] Avg episode reward: [(0, '7.742')] -[2023-02-23 23:20:02,689][11085] Saving new best policy, reward=7.742! -[2023-02-23 23:20:04,323][11099] Updated weights for policy 0, policy_version 450 (0.0011) -[2023-02-23 23:20:07,677][00448] Fps is (10 sec: 3686.8, 60 sec: 3549.9, 300 sec: 3679.5). Total num frames: 1851392. Throughput: 0: 908.0. Samples: 463118. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:20:07,682][00448] Avg episode reward: [(0, '8.163')] -[2023-02-23 23:20:07,696][11085] Saving new best policy, reward=8.163! -[2023-02-23 23:20:12,677][00448] Fps is (10 sec: 3277.5, 60 sec: 3618.1, 300 sec: 3651.7). Total num frames: 1867776. Throughput: 0: 881.3. Samples: 465168. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:20:12,680][00448] Avg episode reward: [(0, '7.221')] -[2023-02-23 23:20:16,116][11099] Updated weights for policy 0, policy_version 460 (0.0027) -[2023-02-23 23:20:17,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3651.7). Total num frames: 1888256. Throughput: 0: 912.8. Samples: 471142. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:20:17,684][00448] Avg episode reward: [(0, '6.704')] -[2023-02-23 23:20:22,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 1908736. Throughput: 0: 940.7. Samples: 477598. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:20:22,684][00448] Avg episode reward: [(0, '7.189')] -[2023-02-23 23:20:27,472][11099] Updated weights for policy 0, policy_version 470 (0.0014) -[2023-02-23 23:20:27,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3618.3, 300 sec: 3665.6). Total num frames: 1925120. Throughput: 0: 918.9. Samples: 479786. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:20:27,681][00448] Avg episode reward: [(0, '7.623')] -[2023-02-23 23:20:32,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3651.7). Total num frames: 1941504. Throughput: 0: 887.2. Samples: 484322. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:20:32,679][00448] Avg episode reward: [(0, '8.474')] -[2023-02-23 23:20:32,687][11085] Saving new best policy, reward=8.474! -[2023-02-23 23:20:37,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3651.7). Total num frames: 1961984. Throughput: 0: 936.8. Samples: 490750. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:20:37,683][00448] Avg episode reward: [(0, '8.259')] -[2023-02-23 23:20:38,037][11099] Updated weights for policy 0, policy_version 480 (0.0013) -[2023-02-23 23:20:42,677][00448] Fps is (10 sec: 4095.9, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 1982464. Throughput: 0: 947.1. Samples: 494012. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:20:42,679][00448] Avg episode reward: [(0, '8.366')] -[2023-02-23 23:20:47,677][00448] Fps is (10 sec: 3276.7, 60 sec: 3618.1, 300 sec: 3651.7). Total num frames: 1994752. Throughput: 0: 899.7. Samples: 498446. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:20:47,680][00448] Avg episode reward: [(0, '8.088')] -[2023-02-23 23:20:50,144][11099] Updated weights for policy 0, policy_version 490 (0.0011) -[2023-02-23 23:20:52,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3651.7). Total num frames: 2015232. Throughput: 0: 909.6. Samples: 504048. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:20:52,682][00448] Avg episode reward: [(0, '8.475')] -[2023-02-23 23:20:57,677][00448] Fps is (10 sec: 4505.7, 60 sec: 3754.7, 300 sec: 3665.6). Total num frames: 2039808. Throughput: 0: 933.9. Samples: 507192. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:20:57,689][00448] Avg episode reward: [(0, '8.439')] -[2023-02-23 23:21:00,236][11099] Updated weights for policy 0, policy_version 500 (0.0018) -[2023-02-23 23:21:02,677][00448] Fps is (10 sec: 3686.3, 60 sec: 3618.2, 300 sec: 3665.6). Total num frames: 2052096. Throughput: 0: 929.9. Samples: 512990. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:21:02,683][00448] Avg episode reward: [(0, '8.589')] -[2023-02-23 23:21:02,685][11085] Saving new best policy, reward=8.589! -[2023-02-23 23:21:07,677][00448] Fps is (10 sec: 2867.3, 60 sec: 3618.1, 300 sec: 3651.7). Total num frames: 2068480. Throughput: 0: 881.0. Samples: 517242. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:21:07,679][00448] Avg episode reward: [(0, '8.642')] -[2023-02-23 23:21:07,692][11085] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000505_2068480.pth... -[2023-02-23 23:21:07,810][11085] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000292_1196032.pth -[2023-02-23 23:21:07,828][11085] Saving new best policy, reward=8.642! -[2023-02-23 23:21:12,094][11099] Updated weights for policy 0, policy_version 510 (0.0016) -[2023-02-23 23:21:12,677][00448] Fps is (10 sec: 3686.6, 60 sec: 3686.4, 300 sec: 3651.7). Total num frames: 2088960. Throughput: 0: 901.4. Samples: 520350. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:21:12,679][00448] Avg episode reward: [(0, '9.510')] -[2023-02-23 23:21:12,689][11085] Saving new best policy, reward=9.510! -[2023-02-23 23:21:17,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 2109440. Throughput: 0: 946.7. Samples: 526922. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:21:17,683][00448] Avg episode reward: [(0, '9.439')] -[2023-02-23 23:21:22,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3618.1, 300 sec: 3679.5). Total num frames: 2125824. Throughput: 0: 911.9. Samples: 531784. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:21:22,683][00448] Avg episode reward: [(0, '8.940')] -[2023-02-23 23:21:23,563][11099] Updated weights for policy 0, policy_version 520 (0.0011) -[2023-02-23 23:21:27,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3618.1, 300 sec: 3651.7). Total num frames: 2142208. Throughput: 0: 888.4. Samples: 533988. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:21:27,683][00448] Avg episode reward: [(0, '8.812')] -[2023-02-23 23:21:32,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3665.6). Total num frames: 2166784. Throughput: 0: 933.4. Samples: 540450. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:21:32,680][00448] Avg episode reward: [(0, '9.224')] -[2023-02-23 23:21:33,481][11099] Updated weights for policy 0, policy_version 530 (0.0020) -[2023-02-23 23:21:37,678][00448] Fps is (10 sec: 4095.4, 60 sec: 3686.3, 300 sec: 3665.6). Total num frames: 2183168. Throughput: 0: 936.5. Samples: 546194. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:21:37,691][00448] Avg episode reward: [(0, '10.552')] -[2023-02-23 23:21:37,703][11085] Saving new best policy, reward=10.552! -[2023-02-23 23:21:42,677][00448] Fps is (10 sec: 2867.2, 60 sec: 3549.9, 300 sec: 3651.7). Total num frames: 2195456. Throughput: 0: 912.8. Samples: 548268. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:21:42,684][00448] Avg episode reward: [(0, '10.440')] -[2023-02-23 23:21:45,840][11099] Updated weights for policy 0, policy_version 540 (0.0021) -[2023-02-23 23:21:47,677][00448] Fps is (10 sec: 3277.3, 60 sec: 3686.4, 300 sec: 3651.7). Total num frames: 2215936. Throughput: 0: 898.5. Samples: 553422. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:21:47,679][00448] Avg episode reward: [(0, '10.709')] -[2023-02-23 23:21:47,690][11085] Saving new best policy, reward=10.709! -[2023-02-23 23:21:52,677][00448] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3665.6). Total num frames: 2240512. Throughput: 0: 948.6. Samples: 559930. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:21:52,680][00448] Avg episode reward: [(0, '11.159')] -[2023-02-23 23:21:52,684][11085] Saving new best policy, reward=11.159! -[2023-02-23 23:21:55,596][11099] Updated weights for policy 0, policy_version 550 (0.0012) -[2023-02-23 23:21:57,680][00448] Fps is (10 sec: 4094.9, 60 sec: 3618.0, 300 sec: 3665.5). Total num frames: 2256896. Throughput: 0: 946.7. Samples: 562956. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:21:57,682][00448] Avg episode reward: [(0, '11.256')] -[2023-02-23 23:21:57,701][11085] Saving new best policy, reward=11.256! -[2023-02-23 23:22:02,677][00448] Fps is (10 sec: 2867.1, 60 sec: 3618.1, 300 sec: 3637.8). Total num frames: 2269184. Throughput: 0: 894.5. Samples: 567176. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:22:02,680][00448] Avg episode reward: [(0, '12.031')] -[2023-02-23 23:22:02,683][11085] Saving new best policy, reward=12.031! -[2023-02-23 23:22:07,510][11099] Updated weights for policy 0, policy_version 560 (0.0011) -[2023-02-23 23:22:07,677][00448] Fps is (10 sec: 3687.4, 60 sec: 3754.7, 300 sec: 3651.7). Total num frames: 2293760. Throughput: 0: 919.6. Samples: 573168. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:22:07,679][00448] Avg episode reward: [(0, '14.228')] -[2023-02-23 23:22:07,688][11085] Saving new best policy, reward=14.228! -[2023-02-23 23:22:12,679][00448] Fps is (10 sec: 4505.0, 60 sec: 3754.6, 300 sec: 3665.6). Total num frames: 2314240. Throughput: 0: 941.4. Samples: 576352. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:22:12,684][00448] Avg episode reward: [(0, '14.440')] -[2023-02-23 23:22:12,690][11085] Saving new best policy, reward=14.440! -[2023-02-23 23:22:17,679][00448] Fps is (10 sec: 3276.0, 60 sec: 3618.0, 300 sec: 3651.7). Total num frames: 2326528. Throughput: 0: 916.3. Samples: 581684. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:22:17,685][00448] Avg episode reward: [(0, '14.446')] -[2023-02-23 23:22:17,713][11085] Saving new best policy, reward=14.446! -[2023-02-23 23:22:19,108][11099] Updated weights for policy 0, policy_version 570 (0.0022) -[2023-02-23 23:22:22,677][00448] Fps is (10 sec: 3277.3, 60 sec: 3686.4, 300 sec: 3651.7). Total num frames: 2347008. Throughput: 0: 893.6. Samples: 586404. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:22:22,682][00448] Avg episode reward: [(0, '14.675')] -[2023-02-23 23:22:22,685][11085] Saving new best policy, reward=14.675! -[2023-02-23 23:22:27,677][00448] Fps is (10 sec: 4097.0, 60 sec: 3754.7, 300 sec: 3637.8). Total num frames: 2367488. Throughput: 0: 921.5. Samples: 589734. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:22:27,679][00448] Avg episode reward: [(0, '14.480')] -[2023-02-23 23:22:29,450][11099] Updated weights for policy 0, policy_version 580 (0.0014) -[2023-02-23 23:22:32,680][00448] Fps is (10 sec: 3685.1, 60 sec: 3617.9, 300 sec: 3637.8). Total num frames: 2383872. Throughput: 0: 945.9. Samples: 595992. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:22:32,683][00448] Avg episode reward: [(0, '15.685')] -[2023-02-23 23:22:32,685][11085] Saving new best policy, reward=15.685! -[2023-02-23 23:22:37,677][00448] Fps is (10 sec: 2867.2, 60 sec: 3549.9, 300 sec: 3623.9). Total num frames: 2396160. Throughput: 0: 878.9. Samples: 599480. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) -[2023-02-23 23:22:37,680][00448] Avg episode reward: [(0, '15.326')] -[2023-02-23 23:22:42,677][00448] Fps is (10 sec: 2458.5, 60 sec: 3549.9, 300 sec: 3596.1). Total num frames: 2408448. Throughput: 0: 851.4. Samples: 601266. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) -[2023-02-23 23:22:42,684][00448] Avg episode reward: [(0, '15.809')] -[2023-02-23 23:22:42,687][11085] Saving new best policy, reward=15.809! -[2023-02-23 23:22:44,777][11099] Updated weights for policy 0, policy_version 590 (0.0015) -[2023-02-23 23:22:47,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3596.2). Total num frames: 2428928. Throughput: 0: 858.8. Samples: 605824. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:22:47,683][00448] Avg episode reward: [(0, '17.286')] -[2023-02-23 23:22:47,693][11085] Saving new best policy, reward=17.286! -[2023-02-23 23:22:52,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3481.6, 300 sec: 3610.0). Total num frames: 2449408. Throughput: 0: 874.5. Samples: 612522. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:22:52,683][00448] Avg episode reward: [(0, '16.911')] -[2023-02-23 23:22:54,184][11099] Updated weights for policy 0, policy_version 600 (0.0012) -[2023-02-23 23:22:57,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3481.8, 300 sec: 3596.1). Total num frames: 2465792. Throughput: 0: 871.2. Samples: 615556. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) -[2023-02-23 23:22:57,685][00448] Avg episode reward: [(0, '16.729')] -[2023-02-23 23:23:02,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3582.3). Total num frames: 2482176. Throughput: 0: 851.3. Samples: 619990. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:23:02,679][00448] Avg episode reward: [(0, '16.567')] -[2023-02-23 23:23:05,901][11099] Updated weights for policy 0, policy_version 610 (0.0017) -[2023-02-23 23:23:07,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3596.1). Total num frames: 2502656. Throughput: 0: 882.0. Samples: 626096. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:23:07,679][00448] Avg episode reward: [(0, '17.245')] -[2023-02-23 23:23:07,742][11085] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000612_2506752.pth... -[2023-02-23 23:23:07,842][11085] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000400_1638400.pth -[2023-02-23 23:23:12,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3481.7, 300 sec: 3623.9). Total num frames: 2523136. Throughput: 0: 875.8. Samples: 629146. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:23:12,679][00448] Avg episode reward: [(0, '16.824')] -[2023-02-23 23:23:17,606][11099] Updated weights for policy 0, policy_version 620 (0.0025) -[2023-02-23 23:23:17,683][00448] Fps is (10 sec: 3684.3, 60 sec: 3549.7, 300 sec: 3610.0). Total num frames: 2539520. Throughput: 0: 848.6. Samples: 634182. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:23:17,685][00448] Avg episode reward: [(0, '15.652')] -[2023-02-23 23:23:22,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3582.3). Total num frames: 2555904. Throughput: 0: 868.6. Samples: 638568. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:23:22,686][00448] Avg episode reward: [(0, '16.224')] -[2023-02-23 23:23:27,677][00448] Fps is (10 sec: 3688.5, 60 sec: 3481.6, 300 sec: 3610.0). Total num frames: 2576384. Throughput: 0: 897.3. Samples: 641644. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:23:27,682][00448] Avg episode reward: [(0, '16.194')] -[2023-02-23 23:23:28,759][11099] Updated weights for policy 0, policy_version 630 (0.0013) -[2023-02-23 23:23:32,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3481.8, 300 sec: 3610.1). Total num frames: 2592768. Throughput: 0: 937.0. Samples: 647990. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) -[2023-02-23 23:23:32,679][00448] Avg episode reward: [(0, '16.252')] -[2023-02-23 23:23:37,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3582.3). Total num frames: 2609152. Throughput: 0: 889.0. Samples: 652526. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:23:37,679][00448] Avg episode reward: [(0, '17.013')] -[2023-02-23 23:23:40,691][11099] Updated weights for policy 0, policy_version 640 (0.0013) -[2023-02-23 23:23:42,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3610.0). Total num frames: 2629632. Throughput: 0: 871.5. Samples: 654772. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:23:42,682][00448] Avg episode reward: [(0, '17.431')] -[2023-02-23 23:23:42,685][11085] Saving new best policy, reward=17.431! -[2023-02-23 23:23:47,677][00448] Fps is (10 sec: 4095.9, 60 sec: 3686.4, 300 sec: 3637.8). Total num frames: 2650112. Throughput: 0: 920.8. Samples: 661426. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:23:47,684][00448] Avg episode reward: [(0, '18.032')] -[2023-02-23 23:23:47,693][11085] Saving new best policy, reward=18.032! -[2023-02-23 23:23:50,177][11099] Updated weights for policy 0, policy_version 650 (0.0017) -[2023-02-23 23:23:52,681][00448] Fps is (10 sec: 3684.7, 60 sec: 3617.9, 300 sec: 3637.7). Total num frames: 2666496. Throughput: 0: 914.3. Samples: 667244. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:23:52,684][00448] Avg episode reward: [(0, '17.729')] -[2023-02-23 23:23:57,677][00448] Fps is (10 sec: 3276.7, 60 sec: 3618.1, 300 sec: 3623.9). Total num frames: 2682880. Throughput: 0: 896.7. Samples: 669500. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:23:57,680][00448] Avg episode reward: [(0, '17.311')] -[2023-02-23 23:24:02,063][11099] Updated weights for policy 0, policy_version 660 (0.0012) -[2023-02-23 23:24:02,677][00448] Fps is (10 sec: 3688.1, 60 sec: 3686.4, 300 sec: 3610.0). Total num frames: 2703360. Throughput: 0: 907.9. Samples: 675034. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:24:02,679][00448] Avg episode reward: [(0, '17.448')] -[2023-02-23 23:24:07,677][00448] Fps is (10 sec: 4505.8, 60 sec: 3754.7, 300 sec: 3651.7). Total num frames: 2727936. Throughput: 0: 960.4. Samples: 681784. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:24:07,682][00448] Avg episode reward: [(0, '18.755')] -[2023-02-23 23:24:07,693][11085] Saving new best policy, reward=18.755! -[2023-02-23 23:24:12,542][11099] Updated weights for policy 0, policy_version 670 (0.0011) -[2023-02-23 23:24:12,679][00448] Fps is (10 sec: 4094.9, 60 sec: 3686.2, 300 sec: 3651.7). Total num frames: 2744320. Throughput: 0: 951.6. Samples: 684468. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:24:12,684][00448] Avg episode reward: [(0, '19.476')] -[2023-02-23 23:24:12,686][11085] Saving new best policy, reward=19.476! -[2023-02-23 23:24:17,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.7, 300 sec: 3637.8). Total num frames: 2760704. Throughput: 0: 909.0. Samples: 688894. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:24:17,683][00448] Avg episode reward: [(0, '20.182')] -[2023-02-23 23:24:17,693][11085] Saving new best policy, reward=20.182! -[2023-02-23 23:24:22,677][00448] Fps is (10 sec: 3687.4, 60 sec: 3754.7, 300 sec: 3637.8). Total num frames: 2781184. Throughput: 0: 952.3. Samples: 695378. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:24:22,679][00448] Avg episode reward: [(0, '19.606')] -[2023-02-23 23:24:23,047][11099] Updated weights for policy 0, policy_version 680 (0.0015) -[2023-02-23 23:24:27,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3665.6). Total num frames: 2801664. Throughput: 0: 976.2. Samples: 698702. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) -[2023-02-23 23:24:27,680][00448] Avg episode reward: [(0, '18.898')] -[2023-02-23 23:24:32,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3651.7). Total num frames: 2818048. Throughput: 0: 940.6. Samples: 703752. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:24:32,681][00448] Avg episode reward: [(0, '18.364')] -[2023-02-23 23:24:35,106][11099] Updated weights for policy 0, policy_version 690 (0.0015) -[2023-02-23 23:24:37,677][00448] Fps is (10 sec: 3276.7, 60 sec: 3754.7, 300 sec: 3637.8). Total num frames: 2834432. Throughput: 0: 926.9. Samples: 708950. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:24:37,685][00448] Avg episode reward: [(0, '17.754')] -[2023-02-23 23:24:42,677][00448] Fps is (10 sec: 4096.1, 60 sec: 3822.9, 300 sec: 3665.6). Total num frames: 2859008. Throughput: 0: 952.1. Samples: 712342. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:24:42,679][00448] Avg episode reward: [(0, '18.640')] -[2023-02-23 23:24:44,029][11099] Updated weights for policy 0, policy_version 700 (0.0020) -[2023-02-23 23:24:47,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3665.6). Total num frames: 2875392. Throughput: 0: 971.7. Samples: 718760. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:24:47,683][00448] Avg episode reward: [(0, '18.454')] -[2023-02-23 23:24:52,678][00448] Fps is (10 sec: 3276.5, 60 sec: 3754.9, 300 sec: 3651.7). Total num frames: 2891776. Throughput: 0: 921.1. Samples: 723234. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:24:52,684][00448] Avg episode reward: [(0, '18.099')] -[2023-02-23 23:24:56,053][11099] Updated weights for policy 0, policy_version 710 (0.0013) -[2023-02-23 23:24:57,677][00448] Fps is (10 sec: 3686.5, 60 sec: 3823.0, 300 sec: 3651.7). Total num frames: 2912256. Throughput: 0: 924.9. Samples: 726088. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:24:57,680][00448] Avg episode reward: [(0, '17.981')] -[2023-02-23 23:25:02,677][00448] Fps is (10 sec: 4096.3, 60 sec: 3822.9, 300 sec: 3665.6). Total num frames: 2932736. Throughput: 0: 969.2. Samples: 732506. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:25:02,680][00448] Avg episode reward: [(0, '16.032')] -[2023-02-23 23:25:07,083][11099] Updated weights for policy 0, policy_version 720 (0.0014) -[2023-02-23 23:25:07,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 2949120. Throughput: 0: 938.9. Samples: 737628. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:25:07,679][00448] Avg episode reward: [(0, '16.424')] -[2023-02-23 23:25:07,690][11085] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000720_2949120.pth... -[2023-02-23 23:25:07,806][11085] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000505_2068480.pth -[2023-02-23 23:25:12,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.6, 300 sec: 3651.7). Total num frames: 2965504. Throughput: 0: 911.2. Samples: 739708. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:25:12,681][00448] Avg episode reward: [(0, '17.320')] -[2023-02-23 23:25:17,664][11099] Updated weights for policy 0, policy_version 730 (0.0017) -[2023-02-23 23:25:17,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3665.6). Total num frames: 2990080. Throughput: 0: 935.5. Samples: 745848. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:25:17,679][00448] Avg episode reward: [(0, '18.150')] -[2023-02-23 23:25:22,677][00448] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3679.5). Total num frames: 3010560. Throughput: 0: 969.8. Samples: 752592. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:25:22,679][00448] Avg episode reward: [(0, '19.034')] -[2023-02-23 23:25:27,679][00448] Fps is (10 sec: 3276.0, 60 sec: 3686.3, 300 sec: 3665.5). Total num frames: 3022848. Throughput: 0: 941.5. Samples: 754710. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:25:27,681][00448] Avg episode reward: [(0, '18.429')] -[2023-02-23 23:25:29,791][11099] Updated weights for policy 0, policy_version 740 (0.0020) -[2023-02-23 23:25:32,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3665.6). Total num frames: 3043328. Throughput: 0: 899.2. Samples: 759226. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:25:32,682][00448] Avg episode reward: [(0, '17.762')] -[2023-02-23 23:25:37,677][00448] Fps is (10 sec: 4097.0, 60 sec: 3823.0, 300 sec: 3665.6). Total num frames: 3063808. Throughput: 0: 947.6. Samples: 765874. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:25:37,679][00448] Avg episode reward: [(0, '17.780')] -[2023-02-23 23:25:39,159][11099] Updated weights for policy 0, policy_version 750 (0.0012) -[2023-02-23 23:25:42,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3693.3). Total num frames: 3084288. Throughput: 0: 958.5. Samples: 769222. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:25:42,684][00448] Avg episode reward: [(0, '17.623')] -[2023-02-23 23:25:47,677][00448] Fps is (10 sec: 3276.7, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 3096576. Throughput: 0: 916.9. Samples: 773766. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:25:47,680][00448] Avg episode reward: [(0, '17.530')] -[2023-02-23 23:25:51,047][11099] Updated weights for policy 0, policy_version 760 (0.0020) -[2023-02-23 23:25:52,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3651.7). Total num frames: 3117056. Throughput: 0: 930.4. Samples: 779496. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:25:52,679][00448] Avg episode reward: [(0, '18.780')] -[2023-02-23 23:25:57,677][00448] Fps is (10 sec: 4096.2, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 3137536. Throughput: 0: 955.6. Samples: 782708. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:25:57,680][00448] Avg episode reward: [(0, '19.732')] -[2023-02-23 23:26:01,626][11099] Updated weights for policy 0, policy_version 770 (0.0019) -[2023-02-23 23:26:02,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3679.5). Total num frames: 3153920. Throughput: 0: 945.7. Samples: 788406. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:26:02,683][00448] Avg episode reward: [(0, '19.363')] -[2023-02-23 23:26:07,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 3170304. Throughput: 0: 895.4. Samples: 792886. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:26:07,679][00448] Avg episode reward: [(0, '20.404')] -[2023-02-23 23:26:07,694][11085] Saving new best policy, reward=20.404! -[2023-02-23 23:26:12,628][11099] Updated weights for policy 0, policy_version 780 (0.0015) -[2023-02-23 23:26:12,677][00448] Fps is (10 sec: 4096.1, 60 sec: 3822.9, 300 sec: 3679.5). Total num frames: 3194880. Throughput: 0: 920.6. Samples: 796136. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:26:12,679][00448] Avg episode reward: [(0, '20.820')] -[2023-02-23 23:26:12,682][11085] Saving new best policy, reward=20.820! -[2023-02-23 23:26:17,680][00448] Fps is (10 sec: 4504.0, 60 sec: 3754.4, 300 sec: 3693.3). Total num frames: 3215360. Throughput: 0: 967.7. Samples: 802778. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:26:17,685][00448] Avg episode reward: [(0, '21.613')] -[2023-02-23 23:26:17,699][11085] Saving new best policy, reward=21.613! -[2023-02-23 23:26:22,681][00448] Fps is (10 sec: 3275.3, 60 sec: 3617.9, 300 sec: 3679.4). Total num frames: 3227648. Throughput: 0: 920.2. Samples: 807288. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:26:22,685][00448] Avg episode reward: [(0, '21.879')] -[2023-02-23 23:26:22,692][11085] Saving new best policy, reward=21.879! -[2023-02-23 23:26:25,203][11099] Updated weights for policy 0, policy_version 790 (0.0011) -[2023-02-23 23:26:27,678][00448] Fps is (10 sec: 2458.1, 60 sec: 3618.2, 300 sec: 3637.8). Total num frames: 3239936. Throughput: 0: 886.3. Samples: 809106. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:26:27,683][00448] Avg episode reward: [(0, '21.070')] -[2023-02-23 23:26:32,677][00448] Fps is (10 sec: 2868.5, 60 sec: 3549.9, 300 sec: 3637.8). Total num frames: 3256320. Throughput: 0: 873.0. Samples: 813052. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:26:32,684][00448] Avg episode reward: [(0, '21.017')] -[2023-02-23 23:26:36,890][11099] Updated weights for policy 0, policy_version 800 (0.0019) -[2023-02-23 23:26:37,677][00448] Fps is (10 sec: 3687.0, 60 sec: 3549.9, 300 sec: 3665.6). Total num frames: 3276800. Throughput: 0: 893.0. Samples: 819682. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:26:37,679][00448] Avg episode reward: [(0, '19.728')] -[2023-02-23 23:26:42,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3651.7). Total num frames: 3293184. Throughput: 0: 882.4. Samples: 822416. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:26:42,683][00448] Avg episode reward: [(0, '18.310')] -[2023-02-23 23:26:47,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3623.9). Total num frames: 3309568. Throughput: 0: 850.4. Samples: 826672. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:26:47,680][00448] Avg episode reward: [(0, '18.071')] -[2023-02-23 23:26:49,141][11099] Updated weights for policy 0, policy_version 810 (0.0038) -[2023-02-23 23:26:52,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3618.1, 300 sec: 3651.7). Total num frames: 3334144. Throughput: 0: 891.7. Samples: 833012. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:26:52,679][00448] Avg episode reward: [(0, '18.272')] -[2023-02-23 23:26:57,677][00448] Fps is (10 sec: 4505.6, 60 sec: 3618.1, 300 sec: 3679.5). Total num frames: 3354624. Throughput: 0: 895.6. Samples: 836438. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:26:57,684][00448] Avg episode reward: [(0, '18.611')] -[2023-02-23 23:26:58,650][11099] Updated weights for policy 0, policy_version 820 (0.0022) -[2023-02-23 23:27:02,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3637.8). Total num frames: 3366912. Throughput: 0: 863.9. Samples: 841650. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:27:02,681][00448] Avg episode reward: [(0, '18.252')] -[2023-02-23 23:27:07,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3618.1, 300 sec: 3637.8). Total num frames: 3387392. Throughput: 0: 879.7. Samples: 846872. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:27:07,680][00448] Avg episode reward: [(0, '18.671')] -[2023-02-23 23:27:07,690][11085] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000827_3387392.pth... -[2023-02-23 23:27:07,806][11085] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000612_2506752.pth -[2023-02-23 23:27:09,950][11099] Updated weights for policy 0, policy_version 830 (0.0012) -[2023-02-23 23:27:12,677][00448] Fps is (10 sec: 4095.9, 60 sec: 3549.8, 300 sec: 3665.6). Total num frames: 3407872. Throughput: 0: 913.0. Samples: 850192. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:27:12,685][00448] Avg episode reward: [(0, '21.207')] -[2023-02-23 23:27:17,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3550.1, 300 sec: 3665.6). Total num frames: 3428352. Throughput: 0: 969.6. Samples: 856684. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:27:17,684][00448] Avg episode reward: [(0, '21.281')] -[2023-02-23 23:27:21,266][11099] Updated weights for policy 0, policy_version 840 (0.0011) -[2023-02-23 23:27:22,677][00448] Fps is (10 sec: 3686.3, 60 sec: 3618.4, 300 sec: 3651.7). Total num frames: 3444736. Throughput: 0: 920.3. Samples: 861098. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:27:22,689][00448] Avg episode reward: [(0, '21.938')] -[2023-02-23 23:27:22,693][11085] Saving new best policy, reward=21.938! -[2023-02-23 23:27:27,677][00448] Fps is (10 sec: 3276.7, 60 sec: 3686.5, 300 sec: 3651.7). Total num frames: 3461120. Throughput: 0: 915.1. Samples: 863596. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:27:27,685][00448] Avg episode reward: [(0, '21.966')] -[2023-02-23 23:27:27,703][11085] Saving new best policy, reward=21.966! -[2023-02-23 23:27:31,787][11099] Updated weights for policy 0, policy_version 850 (0.0011) -[2023-02-23 23:27:32,677][00448] Fps is (10 sec: 3686.5, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 3481600. Throughput: 0: 958.5. Samples: 869804. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:27:32,684][00448] Avg episode reward: [(0, '20.330')] -[2023-02-23 23:27:37,677][00448] Fps is (10 sec: 3686.5, 60 sec: 3686.4, 300 sec: 3693.3). Total num frames: 3497984. Throughput: 0: 932.9. Samples: 874994. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:27:37,681][00448] Avg episode reward: [(0, '19.176')] -[2023-02-23 23:27:42,680][00448] Fps is (10 sec: 3275.9, 60 sec: 3686.2, 300 sec: 3679.4). Total num frames: 3514368. Throughput: 0: 906.4. Samples: 877228. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:27:42,682][00448] Avg episode reward: [(0, '17.990')] -[2023-02-23 23:27:44,014][11099] Updated weights for policy 0, policy_version 860 (0.0018) -[2023-02-23 23:27:47,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 3534848. Throughput: 0: 923.2. Samples: 883196. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:27:47,686][00448] Avg episode reward: [(0, '16.382')] -[2023-02-23 23:27:52,683][00448] Fps is (10 sec: 4503.9, 60 sec: 3754.3, 300 sec: 3707.1). Total num frames: 3559424. Throughput: 0: 953.4. Samples: 889780. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:27:52,691][00448] Avg episode reward: [(0, '17.212')] -[2023-02-23 23:27:53,724][11099] Updated weights for policy 0, policy_version 870 (0.0011) -[2023-02-23 23:27:57,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3618.1, 300 sec: 3693.3). Total num frames: 3571712. Throughput: 0: 929.5. Samples: 892020. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:27:57,680][00448] Avg episode reward: [(0, '18.457')] -[2023-02-23 23:28:02,677][00448] Fps is (10 sec: 3278.8, 60 sec: 3754.7, 300 sec: 3693.3). Total num frames: 3592192. Throughput: 0: 885.1. Samples: 896514. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:28:02,680][00448] Avg episode reward: [(0, '19.120')] -[2023-02-23 23:28:05,407][11099] Updated weights for policy 0, policy_version 880 (0.0011) -[2023-02-23 23:28:07,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3693.3). Total num frames: 3612672. Throughput: 0: 935.4. Samples: 903192. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:28:07,682][00448] Avg episode reward: [(0, '19.948')] -[2023-02-23 23:28:12,677][00448] Fps is (10 sec: 4096.1, 60 sec: 3754.7, 300 sec: 3707.3). Total num frames: 3633152. Throughput: 0: 952.9. Samples: 906474. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:28:12,682][00448] Avg episode reward: [(0, '21.699')] -[2023-02-23 23:28:16,475][11099] Updated weights for policy 0, policy_version 890 (0.0013) -[2023-02-23 23:28:17,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3618.1, 300 sec: 3693.3). Total num frames: 3645440. Throughput: 0: 919.0. Samples: 911160. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:28:17,683][00448] Avg episode reward: [(0, '22.236')] -[2023-02-23 23:28:17,694][11085] Saving new best policy, reward=22.236! -[2023-02-23 23:28:22,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3693.3). Total num frames: 3665920. Throughput: 0: 926.3. Samples: 916676. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) -[2023-02-23 23:28:22,682][00448] Avg episode reward: [(0, '22.584')] -[2023-02-23 23:28:22,685][11085] Saving new best policy, reward=22.584! -[2023-02-23 23:28:26,702][11099] Updated weights for policy 0, policy_version 900 (0.0015) -[2023-02-23 23:28:27,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 3686400. Throughput: 0: 948.4. Samples: 919904. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:28:27,683][00448] Avg episode reward: [(0, '22.822')] -[2023-02-23 23:28:27,698][11085] Saving new best policy, reward=22.822! -[2023-02-23 23:28:32,677][00448] Fps is (10 sec: 3686.2, 60 sec: 3686.4, 300 sec: 3707.2). Total num frames: 3702784. Throughput: 0: 940.7. Samples: 925530. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:28:32,682][00448] Avg episode reward: [(0, '23.394')] -[2023-02-23 23:28:32,685][11085] Saving new best policy, reward=23.394! -[2023-02-23 23:28:37,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3693.3). Total num frames: 3719168. Throughput: 0: 887.2. Samples: 929698. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:28:37,680][00448] Avg episode reward: [(0, '22.679')] -[2023-02-23 23:28:39,423][11099] Updated weights for policy 0, policy_version 910 (0.0012) -[2023-02-23 23:28:42,677][00448] Fps is (10 sec: 3686.6, 60 sec: 3754.8, 300 sec: 3693.3). Total num frames: 3739648. Throughput: 0: 904.2. Samples: 932708. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:28:42,680][00448] Avg episode reward: [(0, '22.645')] -[2023-02-23 23:28:47,681][00448] Fps is (10 sec: 4094.6, 60 sec: 3754.4, 300 sec: 3707.2). Total num frames: 3760128. Throughput: 0: 947.3. Samples: 939146. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:28:47,683][00448] Avg episode reward: [(0, '21.734')] -[2023-02-23 23:28:49,670][11099] Updated weights for policy 0, policy_version 920 (0.0014) -[2023-02-23 23:28:52,682][00448] Fps is (10 sec: 3275.0, 60 sec: 3549.9, 300 sec: 3693.3). Total num frames: 3772416. Throughput: 0: 903.9. Samples: 943874. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) -[2023-02-23 23:28:52,685][00448] Avg episode reward: [(0, '21.531')] -[2023-02-23 23:28:57,677][00448] Fps is (10 sec: 3278.0, 60 sec: 3686.4, 300 sec: 3693.3). Total num frames: 3792896. Throughput: 0: 881.7. Samples: 946150. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) -[2023-02-23 23:28:57,685][00448] Avg episode reward: [(0, '21.153')] -[2023-02-23 23:29:00,985][11099] Updated weights for policy 0, policy_version 930 (0.0013) -[2023-02-23 23:29:02,677][00448] Fps is (10 sec: 4098.3, 60 sec: 3686.4, 300 sec: 3679.5). Total num frames: 3813376. Throughput: 0: 919.5. Samples: 952536. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:29:02,680][00448] Avg episode reward: [(0, '19.943')] -[2023-02-23 23:29:07,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3693.4). Total num frames: 3833856. Throughput: 0: 935.6. Samples: 958776. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:29:07,680][00448] Avg episode reward: [(0, '19.738')] -[2023-02-23 23:29:07,694][11085] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000936_3833856.pth... -[2023-02-23 23:29:07,819][11085] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000720_2949120.pth -[2023-02-23 23:29:12,680][00448] Fps is (10 sec: 3275.9, 60 sec: 3549.7, 300 sec: 3679.4). Total num frames: 3846144. Throughput: 0: 911.5. Samples: 960922. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:29:12,687][00448] Avg episode reward: [(0, '20.773')] -[2023-02-23 23:29:12,881][11099] Updated weights for policy 0, policy_version 940 (0.0021) -[2023-02-23 23:29:17,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3679.5). Total num frames: 3866624. Throughput: 0: 895.7. Samples: 965836. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:29:17,685][00448] Avg episode reward: [(0, '19.710')] -[2023-02-23 23:29:22,587][11099] Updated weights for policy 0, policy_version 950 (0.0015) -[2023-02-23 23:29:22,678][00448] Fps is (10 sec: 4506.1, 60 sec: 3754.6, 300 sec: 3693.3). Total num frames: 3891200. Throughput: 0: 948.5. Samples: 972384. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:29:22,685][00448] Avg episode reward: [(0, '19.276')] -[2023-02-23 23:29:27,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3693.3). Total num frames: 3907584. Throughput: 0: 948.9. Samples: 975410. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:29:27,682][00448] Avg episode reward: [(0, '19.610')] -[2023-02-23 23:29:32,677][00448] Fps is (10 sec: 2867.6, 60 sec: 3618.2, 300 sec: 3679.5). Total num frames: 3919872. Throughput: 0: 897.9. Samples: 979550. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:29:32,687][00448] Avg episode reward: [(0, '19.652')] -[2023-02-23 23:29:35,166][11099] Updated weights for policy 0, policy_version 960 (0.0017) -[2023-02-23 23:29:37,677][00448] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 3940352. Throughput: 0: 922.0. Samples: 985360. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:29:37,679][00448] Avg episode reward: [(0, '18.707')] -[2023-02-23 23:29:42,677][00448] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3679.5). Total num frames: 3960832. Throughput: 0: 942.9. Samples: 988580. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:29:42,685][00448] Avg episode reward: [(0, '20.170')] -[2023-02-23 23:29:45,161][11099] Updated weights for policy 0, policy_version 970 (0.0012) -[2023-02-23 23:29:47,677][00448] Fps is (10 sec: 3686.4, 60 sec: 3618.3, 300 sec: 3679.5). Total num frames: 3977216. Throughput: 0: 923.9. Samples: 994112. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) -[2023-02-23 23:29:47,680][00448] Avg episode reward: [(0, '20.482')] -[2023-02-23 23:29:52,693][00448] Fps is (10 sec: 3680.5, 60 sec: 3754.0, 300 sec: 3679.3). Total num frames: 3997696. Throughput: 0: 889.9. Samples: 998834. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) -[2023-02-23 23:29:52,695][00448] Avg episode reward: [(0, '20.685')] -[2023-02-23 23:29:54,557][11085] Stopping Batcher_0... -[2023-02-23 23:29:54,558][11085] Loop batcher_evt_loop terminating... -[2023-02-23 23:29:54,560][11085] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... -[2023-02-23 23:29:54,558][00448] Component Batcher_0 stopped! -[2023-02-23 23:29:54,562][00448] Component RolloutWorker_w1 process died already! Don't wait for it. -[2023-02-23 23:29:54,570][00448] Component RolloutWorker_w3 process died already! Don't wait for it. -[2023-02-23 23:29:54,608][11099] Weights refcount: 2 0 -[2023-02-23 23:29:54,612][00448] Component InferenceWorker_p0-w0 stopped! -[2023-02-23 23:29:54,616][11099] Stopping InferenceWorker_p0-w0... -[2023-02-23 23:29:54,619][11099] Loop inference_proc0-0_evt_loop terminating... -[2023-02-23 23:29:54,638][00448] Component RolloutWorker_w5 stopped! -[2023-02-23 23:29:54,640][11105] Stopping RolloutWorker_w5... -[2023-02-23 23:29:54,647][00448] Component RolloutWorker_w0 stopped! -[2023-02-23 23:29:54,654][00448] Component RolloutWorker_w7 stopped! -[2023-02-23 23:29:54,657][00448] Component RolloutWorker_w4 stopped! -[2023-02-23 23:29:54,659][11107] Stopping RolloutWorker_w7... -[2023-02-23 23:29:54,643][11105] Loop rollout_proc5_evt_loop terminating... -[2023-02-23 23:29:54,665][11106] Stopping RolloutWorker_w6... -[2023-02-23 23:29:54,665][11106] Loop rollout_proc6_evt_loop terminating... -[2023-02-23 23:29:54,664][00448] Component RolloutWorker_w6 stopped! -[2023-02-23 23:29:54,656][11104] Stopping RolloutWorker_w4... -[2023-02-23 23:29:54,670][11107] Loop rollout_proc7_evt_loop terminating... -[2023-02-23 23:29:54,647][11100] Stopping RolloutWorker_w0... -[2023-02-23 23:29:54,677][11102] Stopping RolloutWorker_w2... -[2023-02-23 23:29:54,678][00448] Component RolloutWorker_w2 stopped! -[2023-02-23 23:29:54,674][11104] Loop rollout_proc4_evt_loop terminating... -[2023-02-23 23:29:54,671][11100] Loop rollout_proc0_evt_loop terminating... -[2023-02-23 23:29:54,679][11102] Loop rollout_proc2_evt_loop terminating... -[2023-02-23 23:29:54,725][11085] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000827_3387392.pth -[2023-02-23 23:29:54,733][11085] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... -[2023-02-23 23:29:54,877][11085] Stopping LearnerWorker_p0... -[2023-02-23 23:29:54,876][00448] Component LearnerWorker_p0 stopped! -[2023-02-23 23:29:54,879][11085] Loop learner_proc0_evt_loop terminating... -[2023-02-23 23:29:54,879][00448] Waiting for process learner_proc0 to stop... -[2023-02-23 23:29:56,358][00448] Waiting for process inference_proc0-0 to join... -[2023-02-23 23:29:56,646][00448] Waiting for process rollout_proc0 to join... -[2023-02-23 23:29:57,068][00448] Waiting for process rollout_proc1 to join... -[2023-02-23 23:29:57,069][00448] Waiting for process rollout_proc2 to join... -[2023-02-23 23:29:57,071][00448] Waiting for process rollout_proc3 to join... -[2023-02-23 23:29:57,072][00448] Waiting for process rollout_proc4 to join... -[2023-02-23 23:29:57,073][00448] Waiting for process rollout_proc5 to join... -[2023-02-23 23:29:57,074][00448] Waiting for process rollout_proc6 to join... -[2023-02-23 23:29:57,078][00448] Waiting for process rollout_proc7 to join... -[2023-02-23 23:29:57,079][00448] Batcher 0 profile tree view: -batching: 24.0729, releasing_batches: 0.0315 -[2023-02-23 23:29:57,085][00448] InferenceWorker_p0-w0 profile tree view: -wait_policy: 0.0000 - wait_policy_total: 504.4362 -update_model: 7.9966 - weight_update: 0.0012 -one_step: 0.0021 - handle_policy_step: 543.1392 - deserialize: 15.4918, stack: 3.3463, obs_to_device_normalize: 124.4127, forward: 263.5132, send_messages: 24.1263 - prepare_outputs: 83.8684 - to_cpu: 52.2079 -[2023-02-23 23:29:57,086][00448] Learner 0 profile tree view: -misc: 0.0060, prepare_batch: 16.1872 -train: 71.7079 - epoch_init: 0.0103, minibatch_init: 0.0067, losses_postprocess: 0.5687, kl_divergence: 0.5242, after_optimizer: 32.0091 - calculate_losses: 25.0010 - losses_init: 0.0035, forward_head: 1.6516, bptt_initial: 16.8612, tail: 0.9269, advantages_returns: 0.2884, losses: 3.1125 - bptt: 1.8923 - bptt_forward_core: 1.8242 - update: 13.0143 - clip: 1.3374 -[2023-02-23 23:29:57,088][00448] RolloutWorker_w0 profile tree view: -wait_for_trajectories: 0.3771, enqueue_policy_requests: 121.3785, env_step: 858.6005, overhead: 22.2514, complete_rollouts: 8.3380 -save_policy_outputs: 22.2601 - split_output_tensors: 10.9670 -[2023-02-23 23:29:57,089][00448] RolloutWorker_w7 profile tree view: -wait_for_trajectories: 0.3809, enqueue_policy_requests: 246.0122, env_step: 723.8699, overhead: 27.1911, complete_rollouts: 5.0990 -save_policy_outputs: 24.1922 - split_output_tensors: 11.7255 -[2023-02-23 23:29:57,091][00448] Loop Runner_EvtLoop terminating... -[2023-02-23 23:29:57,093][00448] Runner profile tree view: -main_loop: 1125.3538 -[2023-02-23 23:29:57,094][00448] Collected {0: 4005888}, FPS: 3559.7 -[2023-02-23 23:29:57,160][00448] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json -[2023-02-23 23:29:57,161][00448] Overriding arg 'num_workers' with value 1 passed from command line -[2023-02-23 23:29:57,164][00448] Adding new argument 'no_render'=True that is not in the saved config file! -[2023-02-23 23:29:57,166][00448] Adding new argument 'save_video'=True that is not in the saved config file! -[2023-02-23 23:29:57,167][00448] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! -[2023-02-23 23:29:57,169][00448] Adding new argument 'video_name'=None that is not in the saved config file! -[2023-02-23 23:29:57,170][00448] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file! -[2023-02-23 23:29:57,172][00448] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! -[2023-02-23 23:29:57,173][00448] Adding new argument 'push_to_hub'=False that is not in the saved config file! -[2023-02-23 23:29:57,175][00448] Adding new argument 'hf_repository'=None that is not in the saved config file! -[2023-02-23 23:29:57,177][00448] Adding new argument 'policy_index'=0 that is not in the saved config file! -[2023-02-23 23:29:57,179][00448] Adding new argument 'eval_deterministic'=False that is not in the saved config file! -[2023-02-23 23:29:57,180][00448] Adding new argument 'train_script'=None that is not in the saved config file! -[2023-02-23 23:29:57,182][00448] Adding new argument 'enjoy_script'=None that is not in the saved config file! -[2023-02-23 23:29:57,183][00448] Using frameskip 1 and render_action_repeat=4 for evaluation -[2023-02-23 23:29:57,210][00448] Doom resolution: 160x120, resize resolution: (128, 72) -[2023-02-23 23:29:57,213][00448] RunningMeanStd input shape: (3, 72, 128) -[2023-02-23 23:29:57,216][00448] RunningMeanStd input shape: (1,) -[2023-02-23 23:29:57,231][00448] ConvEncoder: input_channels=3 -[2023-02-23 23:29:57,909][00448] Conv encoder output size: 512 -[2023-02-23 23:29:57,911][00448] Policy head output size: 512 -[2023-02-23 23:30:00,247][00448] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... -[2023-02-23 23:30:01,498][00448] Num frames 100... -[2023-02-23 23:30:01,652][00448] Num frames 200... -[2023-02-23 23:30:01,813][00448] Num frames 300... -[2023-02-23 23:30:01,968][00448] Num frames 400... -[2023-02-23 23:30:02,126][00448] Num frames 500... -[2023-02-23 23:30:02,276][00448] Num frames 600... -[2023-02-23 23:30:02,429][00448] Num frames 700... -[2023-02-23 23:30:02,586][00448] Num frames 800... -[2023-02-23 23:30:02,743][00448] Num frames 900... -[2023-02-23 23:30:02,910][00448] Num frames 1000... -[2023-02-23 23:30:03,066][00448] Num frames 1100... -[2023-02-23 23:30:03,221][00448] Num frames 1200... -[2023-02-23 23:30:03,376][00448] Num frames 1300... -[2023-02-23 23:30:03,535][00448] Num frames 1400... -[2023-02-23 23:30:03,608][00448] Avg episode rewards: #0: 31.080, true rewards: #0: 14.080 -[2023-02-23 23:30:03,610][00448] Avg episode reward: 31.080, avg true_objective: 14.080 -[2023-02-23 23:30:03,755][00448] Num frames 1500... -[2023-02-23 23:30:03,913][00448] Num frames 1600... -[2023-02-23 23:30:04,072][00448] Num frames 1700... -[2023-02-23 23:30:04,240][00448] Num frames 1800... -[2023-02-23 23:30:04,406][00448] Num frames 1900... -[2023-02-23 23:30:04,569][00448] Num frames 2000... -[2023-02-23 23:30:04,730][00448] Num frames 2100... -[2023-02-23 23:30:04,893][00448] Num frames 2200... -[2023-02-23 23:30:05,017][00448] Num frames 2300... -[2023-02-23 23:30:05,126][00448] Num frames 2400... -[2023-02-23 23:30:05,246][00448] Num frames 2500... -[2023-02-23 23:30:05,355][00448] Num frames 2600... -[2023-02-23 23:30:05,475][00448] Num frames 2700... -[2023-02-23 23:30:05,584][00448] Num frames 2800... -[2023-02-23 23:30:05,697][00448] Num frames 2900... -[2023-02-23 23:30:05,808][00448] Num frames 3000... -[2023-02-23 23:30:05,924][00448] Num frames 3100... -[2023-02-23 23:30:06,040][00448] Num frames 3200... -[2023-02-23 23:30:06,149][00448] Num frames 3300... -[2023-02-23 23:30:06,268][00448] Num frames 3400... -[2023-02-23 23:30:06,384][00448] Num frames 3500... -[2023-02-23 23:30:06,450][00448] Avg episode rewards: #0: 42.540, true rewards: #0: 17.540 -[2023-02-23 23:30:06,451][00448] Avg episode reward: 42.540, avg true_objective: 17.540 -[2023-02-23 23:30:06,553][00448] Num frames 3600... -[2023-02-23 23:30:06,665][00448] Num frames 3700... -[2023-02-23 23:30:06,785][00448] Num frames 3800... -[2023-02-23 23:30:06,896][00448] Num frames 3900... -[2023-02-23 23:30:07,005][00448] Num frames 4000... -[2023-02-23 23:30:07,114][00448] Num frames 4100... -[2023-02-23 23:30:07,232][00448] Num frames 4200... -[2023-02-23 23:30:07,346][00448] Num frames 4300... -[2023-02-23 23:30:07,493][00448] Num frames 4400... -[2023-02-23 23:30:07,648][00448] Num frames 4500... -[2023-02-23 23:30:07,807][00448] Num frames 4600... -[2023-02-23 23:30:07,964][00448] Num frames 4700... -[2023-02-23 23:30:08,121][00448] Num frames 4800... -[2023-02-23 23:30:08,278][00448] Num frames 4900... -[2023-02-23 23:30:08,447][00448] Num frames 5000... -[2023-02-23 23:30:08,605][00448] Num frames 5100... -[2023-02-23 23:30:08,766][00448] Num frames 5200... -[2023-02-23 23:30:08,937][00448] Num frames 5300... -[2023-02-23 23:30:09,104][00448] Num frames 5400... -[2023-02-23 23:30:09,276][00448] Num frames 5500... -[2023-02-23 23:30:09,440][00448] Num frames 5600... -[2023-02-23 23:30:09,513][00448] Avg episode rewards: #0: 45.359, true rewards: #0: 18.693 -[2023-02-23 23:30:09,516][00448] Avg episode reward: 45.359, avg true_objective: 18.693 -[2023-02-23 23:30:09,673][00448] Num frames 5700... -[2023-02-23 23:30:09,835][00448] Num frames 5800... -[2023-02-23 23:30:09,992][00448] Num frames 5900... -[2023-02-23 23:30:10,170][00448] Num frames 6000... -[2023-02-23 23:30:10,324][00448] Num frames 6100... -[2023-02-23 23:30:10,484][00448] Num frames 6200... -[2023-02-23 23:30:10,641][00448] Num frames 6300... -[2023-02-23 23:30:10,785][00448] Num frames 6400... -[2023-02-23 23:30:10,899][00448] Num frames 6500... -[2023-02-23 23:30:11,015][00448] Num frames 6600... -[2023-02-23 23:30:11,136][00448] Num frames 6700... -[2023-02-23 23:30:11,246][00448] Num frames 6800... -[2023-02-23 23:30:11,355][00448] Num frames 6900... -[2023-02-23 23:30:11,477][00448] Num frames 7000... -[2023-02-23 23:30:11,590][00448] Num frames 7100... -[2023-02-23 23:30:11,701][00448] Num frames 7200... -[2023-02-23 23:30:11,811][00448] Num frames 7300... -[2023-02-23 23:30:11,926][00448] Num frames 7400... -[2023-02-23 23:30:12,020][00448] Avg episode rewards: #0: 45.834, true rewards: #0: 18.585 -[2023-02-23 23:30:12,022][00448] Avg episode reward: 45.834, avg true_objective: 18.585 -[2023-02-23 23:30:12,099][00448] Num frames 7500... -[2023-02-23 23:30:12,211][00448] Num frames 7600... -[2023-02-23 23:30:12,320][00448] Num frames 7700... -[2023-02-23 23:30:12,434][00448] Num frames 7800... -[2023-02-23 23:30:12,544][00448] Num frames 7900... -[2023-02-23 23:30:12,660][00448] Num frames 8000... -[2023-02-23 23:30:12,770][00448] Num frames 8100... -[2023-02-23 23:30:12,882][00448] Num frames 8200... -[2023-02-23 23:30:12,992][00448] Num frames 8300... -[2023-02-23 23:30:13,159][00448] Avg episode rewards: #0: 40.988, true rewards: #0: 16.788 -[2023-02-23 23:30:13,160][00448] Avg episode reward: 40.988, avg true_objective: 16.788 -[2023-02-23 23:30:13,173][00448] Num frames 8400... -[2023-02-23 23:30:13,285][00448] Num frames 8500... -[2023-02-23 23:30:13,394][00448] Num frames 8600... -[2023-02-23 23:30:13,512][00448] Num frames 8700... -[2023-02-23 23:30:13,624][00448] Num frames 8800... -[2023-02-23 23:30:13,734][00448] Num frames 8900... -[2023-02-23 23:30:13,848][00448] Num frames 9000... -[2023-02-23 23:30:13,907][00448] Avg episode rewards: #0: 36.170, true rewards: #0: 15.003 -[2023-02-23 23:30:13,910][00448] Avg episode reward: 36.170, avg true_objective: 15.003 -[2023-02-23 23:30:14,017][00448] Num frames 9100... -[2023-02-23 23:30:14,134][00448] Num frames 9200... -[2023-02-23 23:30:14,244][00448] Num frames 9300... -[2023-02-23 23:30:14,352][00448] Num frames 9400... -[2023-02-23 23:30:14,469][00448] Num frames 9500... -[2023-02-23 23:30:14,582][00448] Num frames 9600... -[2023-02-23 23:30:14,688][00448] Num frames 9700... -[2023-02-23 23:30:14,797][00448] Num frames 9800... -[2023-02-23 23:30:14,905][00448] Num frames 9900... -[2023-02-23 23:30:15,061][00448] Num frames 10000... -[2023-02-23 23:30:15,213][00448] Num frames 10100... -[2023-02-23 23:30:15,402][00448] Avg episode rewards: #0: 34.551, true rewards: #0: 14.551 -[2023-02-23 23:30:15,404][00448] Avg episode reward: 34.551, avg true_objective: 14.551 -[2023-02-23 23:30:15,432][00448] Num frames 10200... -[2023-02-23 23:30:15,598][00448] Num frames 10300... -[2023-02-23 23:30:15,760][00448] Num frames 10400... -[2023-02-23 23:30:15,836][00448] Avg episode rewards: #0: 30.637, true rewards: #0: 13.013 -[2023-02-23 23:30:15,838][00448] Avg episode reward: 30.637, avg true_objective: 13.013 -[2023-02-23 23:30:15,978][00448] Num frames 10500... -[2023-02-23 23:30:16,144][00448] Num frames 10600... -[2023-02-23 23:30:16,298][00448] Num frames 10700... -[2023-02-23 23:30:16,455][00448] Num frames 10800... -[2023-02-23 23:30:16,619][00448] Num frames 10900... -[2023-02-23 23:30:16,775][00448] Num frames 11000... -[2023-02-23 23:30:16,948][00448] Num frames 11100... -[2023-02-23 23:30:17,107][00448] Num frames 11200... -[2023-02-23 23:30:17,262][00448] Num frames 11300... -[2023-02-23 23:30:17,433][00448] Avg episode rewards: #0: 29.633, true rewards: #0: 12.633 -[2023-02-23 23:30:17,436][00448] Avg episode reward: 29.633, avg true_objective: 12.633 -[2023-02-23 23:30:17,483][00448] Num frames 11400... -[2023-02-23 23:30:17,646][00448] Num frames 11500... -[2023-02-23 23:30:17,802][00448] Num frames 11600... -[2023-02-23 23:30:17,956][00448] Num frames 11700... -[2023-02-23 23:30:18,112][00448] Num frames 11800... -[2023-02-23 23:30:18,267][00448] Num frames 11900... -[2023-02-23 23:30:18,395][00448] Num frames 12000... -[2023-02-23 23:30:18,505][00448] Num frames 12100... -[2023-02-23 23:30:18,621][00448] Num frames 12200... -[2023-02-23 23:30:18,735][00448] Num frames 12300... -[2023-02-23 23:30:18,852][00448] Num frames 12400... -[2023-02-23 23:30:18,963][00448] Num frames 12500... -[2023-02-23 23:30:19,073][00448] Num frames 12600... -[2023-02-23 23:30:19,184][00448] Num frames 12700... -[2023-02-23 23:30:19,324][00448] Avg episode rewards: #0: 29.878, true rewards: #0: 12.778 -[2023-02-23 23:30:19,326][00448] Avg episode reward: 29.878, avg true_objective: 12.778 -[2023-02-23 23:31:32,468][00448] Replay video saved to /content/train_dir/default_experiment/replay.mp4! -[2023-02-23 23:31:32,895][00448] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json -[2023-02-23 23:31:32,897][00448] Overriding arg 'num_workers' with value 1 passed from command line -[2023-02-23 23:31:32,900][00448] Adding new argument 'no_render'=True that is not in the saved config file! -[2023-02-23 23:31:32,902][00448] Adding new argument 'save_video'=True that is not in the saved config file! -[2023-02-23 23:31:32,904][00448] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! -[2023-02-23 23:31:32,906][00448] Adding new argument 'video_name'=None that is not in the saved config file! -[2023-02-23 23:31:32,909][00448] Adding new argument 'max_num_frames'=100000 that is not in the saved config file! -[2023-02-23 23:31:32,910][00448] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! -[2023-02-23 23:31:32,911][00448] Adding new argument 'push_to_hub'=True that is not in the saved config file! -[2023-02-23 23:31:32,912][00448] Adding new argument 'hf_repository'='eldraco/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file! -[2023-02-23 23:31:32,913][00448] Adding new argument 'policy_index'=0 that is not in the saved config file! -[2023-02-23 23:31:32,914][00448] Adding new argument 'eval_deterministic'=False that is not in the saved config file! -[2023-02-23 23:31:32,916][00448] Adding new argument 'train_script'=None that is not in the saved config file! -[2023-02-23 23:31:32,917][00448] Adding new argument 'enjoy_script'=None that is not in the saved config file! -[2023-02-23 23:31:32,918][00448] Using frameskip 1 and render_action_repeat=4 for evaluation -[2023-02-23 23:31:32,942][00448] RunningMeanStd input shape: (3, 72, 128) -[2023-02-23 23:31:32,945][00448] RunningMeanStd input shape: (1,) -[2023-02-23 23:31:32,963][00448] ConvEncoder: input_channels=3 -[2023-02-23 23:31:33,017][00448] Conv encoder output size: 512 -[2023-02-23 23:31:33,019][00448] Policy head output size: 512 -[2023-02-23 23:31:33,045][00448] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... -[2023-02-23 23:31:33,763][00448] Num frames 100... -[2023-02-23 23:31:33,913][00448] Num frames 200... -[2023-02-23 23:31:34,066][00448] Num frames 300... -[2023-02-23 23:31:34,229][00448] Num frames 400... -[2023-02-23 23:31:34,378][00448] Num frames 500... -[2023-02-23 23:31:34,533][00448] Num frames 600... -[2023-02-23 23:31:34,684][00448] Num frames 700... -[2023-02-23 23:31:34,836][00448] Num frames 800... -[2023-02-23 23:31:35,014][00448] Num frames 900... -[2023-02-23 23:31:35,165][00448] Num frames 1000... -[2023-02-23 23:31:35,318][00448] Num frames 1100... -[2023-02-23 23:31:35,471][00448] Num frames 1200... -[2023-02-23 23:31:35,632][00448] Num frames 1300... -[2023-02-23 23:31:35,780][00448] Num frames 1400... -[2023-02-23 23:31:35,933][00448] Num frames 1500... -[2023-02-23 23:31:36,089][00448] Num frames 1600... -[2023-02-23 23:31:36,266][00448] Num frames 1700... -[2023-02-23 23:31:36,439][00448] Num frames 1800... -[2023-02-23 23:31:36,613][00448] Num frames 1900... -[2023-02-23 23:31:36,783][00448] Num frames 2000... -[2023-02-23 23:31:36,961][00448] Num frames 2100... -[2023-02-23 23:31:37,016][00448] Avg episode rewards: #0: 54.999, true rewards: #0: 21.000 -[2023-02-23 23:31:37,019][00448] Avg episode reward: 54.999, avg true_objective: 21.000 -[2023-02-23 23:31:37,197][00448] Num frames 2200... -[2023-02-23 23:31:37,397][00448] Num frames 2300... -[2023-02-23 23:31:37,514][00448] Avg episode rewards: #0: 29.120, true rewards: #0: 11.620 -[2023-02-23 23:31:37,516][00448] Avg episode reward: 29.120, avg true_objective: 11.620 -[2023-02-23 23:31:37,680][00448] Num frames 2400... -[2023-02-23 23:31:37,863][00448] Num frames 2500... -[2023-02-23 23:31:38,036][00448] Num frames 2600... -[2023-02-23 23:31:38,188][00448] Num frames 2700... -[2023-02-23 23:31:38,340][00448] Num frames 2800... -[2023-02-23 23:31:38,492][00448] Num frames 2900... -[2023-02-23 23:31:38,646][00448] Num frames 3000... -[2023-02-23 23:31:38,805][00448] Num frames 3100... -[2023-02-23 23:31:38,994][00448] Num frames 3200... -[2023-02-23 23:31:39,180][00448] Avg episode rewards: #0: 27.240, true rewards: #0: 10.907 -[2023-02-23 23:31:39,183][00448] Avg episode reward: 27.240, avg true_objective: 10.907 -[2023-02-23 23:31:39,239][00448] Num frames 3300... -[2023-02-23 23:31:39,422][00448] Num frames 3400... -[2023-02-23 23:31:39,601][00448] Num frames 3500... -[2023-02-23 23:31:39,777][00448] Num frames 3600... -[2023-02-23 23:31:39,951][00448] Num frames 3700... -[2023-02-23 23:31:40,131][00448] Num frames 3800... -[2023-02-23 23:31:40,303][00448] Num frames 3900... -[2023-02-23 23:31:40,475][00448] Num frames 4000... -[2023-02-23 23:31:40,638][00448] Num frames 4100... -[2023-02-23 23:31:40,809][00448] Num frames 4200... -[2023-02-23 23:31:40,980][00448] Num frames 4300... -[2023-02-23 23:31:41,142][00448] Num frames 4400... -[2023-02-23 23:31:41,300][00448] Num frames 4500... -[2023-02-23 23:31:41,457][00448] Num frames 4600... -[2023-02-23 23:31:41,618][00448] Num frames 4700... -[2023-02-23 23:31:41,777][00448] Num frames 4800... -[2023-02-23 23:31:41,941][00448] Num frames 4900... -[2023-02-23 23:31:42,006][00448] Avg episode rewards: #0: 31.260, true rewards: #0: 12.260 -[2023-02-23 23:31:42,008][00448] Avg episode reward: 31.260, avg true_objective: 12.260 -[2023-02-23 23:31:42,169][00448] Num frames 5000... -[2023-02-23 23:31:42,320][00448] Num frames 5100... -[2023-02-23 23:31:42,472][00448] Num frames 5200... -[2023-02-23 23:31:42,636][00448] Num frames 5300... -[2023-02-23 23:31:42,793][00448] Num frames 5400... -[2023-02-23 23:31:42,957][00448] Num frames 5500... -[2023-02-23 23:31:43,079][00448] Num frames 5600... -[2023-02-23 23:31:43,188][00448] Num frames 5700... -[2023-02-23 23:31:43,296][00448] Num frames 5800... -[2023-02-23 23:31:43,407][00448] Num frames 5900... -[2023-02-23 23:31:43,562][00448] Avg episode rewards: #0: 30.578, true rewards: #0: 11.978 -[2023-02-23 23:31:43,563][00448] Avg episode reward: 30.578, avg true_objective: 11.978 -[2023-02-23 23:31:43,578][00448] Num frames 6000... -[2023-02-23 23:31:43,689][00448] Num frames 6100... -[2023-02-23 23:31:43,798][00448] Num frames 6200... -[2023-02-23 23:31:43,914][00448] Num frames 6300... -[2023-02-23 23:31:44,023][00448] Num frames 6400... -[2023-02-23 23:31:44,131][00448] Num frames 6500... -[2023-02-23 23:31:44,238][00448] Num frames 6600... -[2023-02-23 23:31:44,395][00448] Avg episode rewards: #0: 27.322, true rewards: #0: 11.155 -[2023-02-23 23:31:44,397][00448] Avg episode reward: 27.322, avg true_objective: 11.155 -[2023-02-23 23:31:44,409][00448] Num frames 6700... -[2023-02-23 23:31:44,517][00448] Num frames 6800... -[2023-02-23 23:31:44,626][00448] Num frames 6900... -[2023-02-23 23:31:44,737][00448] Num frames 7000... -[2023-02-23 23:31:44,849][00448] Num frames 7100... -[2023-02-23 23:31:44,964][00448] Num frames 7200... -[2023-02-23 23:31:45,073][00448] Num frames 7300... -[2023-02-23 23:31:45,133][00448] Avg episode rewards: #0: 25.290, true rewards: #0: 10.433 -[2023-02-23 23:31:45,136][00448] Avg episode reward: 25.290, avg true_objective: 10.433 -[2023-02-23 23:31:45,240][00448] Num frames 7400... -[2023-02-23 23:31:45,348][00448] Num frames 7500... -[2023-02-23 23:31:45,455][00448] Num frames 7600... -[2023-02-23 23:31:45,566][00448] Num frames 7700... -[2023-02-23 23:31:45,675][00448] Num frames 7800... -[2023-02-23 23:31:45,755][00448] Avg episode rewards: #0: 23.026, true rewards: #0: 9.776 -[2023-02-23 23:31:45,757][00448] Avg episode reward: 23.026, avg true_objective: 9.776 -[2023-02-23 23:31:45,844][00448] Num frames 7900... -[2023-02-23 23:31:45,955][00448] Num frames 8000... -[2023-02-23 23:31:46,062][00448] Num frames 8100... -[2023-02-23 23:31:46,169][00448] Num frames 8200... -[2023-02-23 23:31:46,286][00448] Num frames 8300... -[2023-02-23 23:31:46,393][00448] Num frames 8400... -[2023-02-23 23:31:46,502][00448] Num frames 8500... -[2023-02-23 23:31:46,608][00448] Num frames 8600... -[2023-02-23 23:31:46,721][00448] Num frames 8700... -[2023-02-23 23:31:46,833][00448] Num frames 8800... -[2023-02-23 23:31:46,949][00448] Num frames 8900... -[2023-02-23 23:31:47,057][00448] Num frames 9000... -[2023-02-23 23:31:47,211][00448] Avg episode rewards: #0: 24.101, true rewards: #0: 10.101 -[2023-02-23 23:31:47,213][00448] Avg episode reward: 24.101, avg true_objective: 10.101 -[2023-02-23 23:31:47,227][00448] Num frames 9100... -[2023-02-23 23:31:47,336][00448] Num frames 9200... -[2023-02-23 23:31:47,448][00448] Num frames 9300... -[2023-02-23 23:31:47,556][00448] Num frames 9400... -[2023-02-23 23:31:47,668][00448] Num frames 9500... -[2023-02-23 23:31:47,777][00448] Num frames 9600... -[2023-02-23 23:31:47,886][00448] Num frames 9700... -[2023-02-23 23:31:48,005][00448] Num frames 9800... -[2023-02-23 23:31:48,114][00448] Num frames 9900... -[2023-02-23 23:31:48,223][00448] Num frames 10000... -[2023-02-23 23:31:48,333][00448] Num frames 10100... -[2023-02-23 23:31:48,444][00448] Num frames 10200... -[2023-02-23 23:31:48,556][00448] Num frames 10300... -[2023-02-23 23:31:48,671][00448] Num frames 10400... -[2023-02-23 23:31:48,783][00448] Num frames 10500... -[2023-02-23 23:31:48,907][00448] Num frames 10600... -[2023-02-23 23:31:49,024][00448] Num frames 10700... -[2023-02-23 23:31:49,134][00448] Num frames 10800... -[2023-02-23 23:31:49,246][00448] Num frames 10900... -[2023-02-23 23:31:49,365][00448] Num frames 11000... -[2023-02-23 23:31:49,504][00448] Num frames 11100... -[2023-02-23 23:31:49,660][00448] Avg episode rewards: #0: 26.991, true rewards: #0: 11.191 -[2023-02-23 23:31:49,663][00448] Avg episode reward: 26.991, avg true_objective: 11.191 -[2023-02-23 23:32:55,407][00448] Replay video saved to /content/train_dir/default_experiment/replay.mp4! +[2023-02-24 10:25:55,899][00397] Heartbeat connected on Batcher_0 +[2023-02-24 10:25:55,906][00397] Heartbeat connected on InferenceWorker_p0-w0 +[2023-02-24 10:25:55,915][00397] Heartbeat connected on RolloutWorker_w0 +[2023-02-24 10:25:55,920][00397] Heartbeat connected on RolloutWorker_w1 +[2023-02-24 10:25:55,922][00397] Heartbeat connected on RolloutWorker_w2 +[2023-02-24 10:25:55,927][00397] Heartbeat connected on RolloutWorker_w3 +[2023-02-24 10:25:55,928][00397] Heartbeat connected on RolloutWorker_w4 +[2023-02-24 10:25:55,934][00397] Heartbeat connected on RolloutWorker_w5 +[2023-02-24 10:25:55,936][00397] Heartbeat connected on RolloutWorker_w6 +[2023-02-24 10:25:55,940][00397] Heartbeat connected on RolloutWorker_w7 +[2023-02-24 10:25:56,669][12747] Using optimizer +[2023-02-24 10:25:56,670][12747] No checkpoints found +[2023-02-24 10:25:56,670][12747] Did not load from checkpoint, starting from scratch! +[2023-02-24 10:25:56,671][12747] Initialized policy 0 weights for model version 0 +[2023-02-24 10:25:56,681][12747] Using GPUs [0] for process 0 (actually maps to GPUs [0]) +[2023-02-24 10:25:56,692][12747] LearnerWorker_p0 finished initialization! +[2023-02-24 10:25:56,693][00397] Heartbeat connected on LearnerWorker_p0 +[2023-02-24 10:25:56,959][12761] RunningMeanStd input shape: (3, 72, 128) +[2023-02-24 10:25:56,961][12761] RunningMeanStd input shape: (1,) +[2023-02-24 10:25:56,979][12761] ConvEncoder: input_channels=3 +[2023-02-24 10:25:57,145][12761] Conv encoder output size: 512 +[2023-02-24 10:25:57,146][12761] Policy head output size: 512 +[2023-02-24 10:25:59,624][00397] Inference worker 0-0 is ready! +[2023-02-24 10:25:59,626][00397] All inference workers are ready! Signal rollout workers to start! +[2023-02-24 10:25:59,759][12768] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 10:25:59,764][12764] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 10:25:59,770][12767] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 10:25:59,777][12765] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 10:25:59,791][12763] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 10:25:59,794][12766] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 10:25:59,795][12762] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 10:25:59,799][12769] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 10:26:00,644][12766] Decorrelating experience for 0 frames... +[2023-02-24 10:26:00,645][12763] Decorrelating experience for 0 frames... +[2023-02-24 10:26:00,890][12765] Decorrelating experience for 0 frames... +[2023-02-24 10:26:00,897][12768] Decorrelating experience for 0 frames... +[2023-02-24 10:26:00,903][12767] Decorrelating experience for 0 frames... +[2023-02-24 10:26:01,542][00397] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 0. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) +[2023-02-24 10:26:01,551][12763] Decorrelating experience for 32 frames... +[2023-02-24 10:26:01,634][12762] Decorrelating experience for 0 frames... +[2023-02-24 10:26:01,928][12766] Decorrelating experience for 32 frames... +[2023-02-24 10:26:01,929][12765] Decorrelating experience for 32 frames... +[2023-02-24 10:26:01,942][12768] Decorrelating experience for 32 frames... +[2023-02-24 10:26:01,946][12767] Decorrelating experience for 32 frames... +[2023-02-24 10:26:02,687][12762] Decorrelating experience for 32 frames... +[2023-02-24 10:26:02,877][12768] Decorrelating experience for 64 frames... +[2023-02-24 10:26:02,887][12767] Decorrelating experience for 64 frames... +[2023-02-24 10:26:03,090][12769] Decorrelating experience for 0 frames... +[2023-02-24 10:26:03,100][12763] Decorrelating experience for 64 frames... +[2023-02-24 10:26:03,701][12762] Decorrelating experience for 64 frames... +[2023-02-24 10:26:03,830][12766] Decorrelating experience for 64 frames... +[2023-02-24 10:26:03,946][12767] Decorrelating experience for 96 frames... +[2023-02-24 10:26:03,950][12768] Decorrelating experience for 96 frames... +[2023-02-24 10:26:04,219][12764] Decorrelating experience for 0 frames... +[2023-02-24 10:26:04,940][12762] Decorrelating experience for 96 frames... +[2023-02-24 10:26:05,056][12766] Decorrelating experience for 96 frames... +[2023-02-24 10:26:05,320][12769] Decorrelating experience for 32 frames... +[2023-02-24 10:26:05,345][12765] Decorrelating experience for 64 frames... +[2023-02-24 10:26:05,365][12763] Decorrelating experience for 96 frames... +[2023-02-24 10:26:06,090][12764] Decorrelating experience for 32 frames... +[2023-02-24 10:26:06,179][12765] Decorrelating experience for 96 frames... +[2023-02-24 10:26:06,237][12769] Decorrelating experience for 64 frames... +[2023-02-24 10:26:06,542][00397] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 0.0. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) +[2023-02-24 10:26:06,653][12764] Decorrelating experience for 64 frames... +[2023-02-24 10:26:06,819][12769] Decorrelating experience for 96 frames... +[2023-02-24 10:26:07,163][12764] Decorrelating experience for 96 frames... +[2023-02-24 10:26:11,542][00397] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 42.6. Samples: 426. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) +[2023-02-24 10:26:11,547][00397] Avg episode reward: [(0, '1.077')] +[2023-02-24 10:26:12,475][12747] Signal inference workers to stop experience collection... +[2023-02-24 10:26:12,500][12761] InferenceWorker_p0-w0: stopping experience collection +[2023-02-24 10:26:14,856][12747] Signal inference workers to resume experience collection... +[2023-02-24 10:26:14,857][12761] InferenceWorker_p0-w0: resuming experience collection +[2023-02-24 10:26:16,542][00397] Fps is (10 sec: 409.6, 60 sec: 273.1, 300 sec: 273.1). Total num frames: 4096. Throughput: 0: 142.9. Samples: 2144. Policy #0 lag: (min: 0.0, avg: 0.0, max: 0.0) +[2023-02-24 10:26:16,549][00397] Avg episode reward: [(0, '1.762')] +[2023-02-24 10:26:21,542][00397] Fps is (10 sec: 2867.2, 60 sec: 1433.6, 300 sec: 1433.6). Total num frames: 28672. Throughput: 0: 351.0. Samples: 7020. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) +[2023-02-24 10:26:21,545][00397] Avg episode reward: [(0, '3.635')] +[2023-02-24 10:26:23,873][12761] Updated weights for policy 0, policy_version 10 (0.0018) +[2023-02-24 10:26:26,542][00397] Fps is (10 sec: 4505.6, 60 sec: 1966.1, 300 sec: 1966.1). Total num frames: 49152. Throughput: 0: 407.7. Samples: 10192. Policy #0 lag: (min: 0.0, avg: 0.1, max: 1.0) +[2023-02-24 10:26:26,546][00397] Avg episode reward: [(0, '4.339')] +[2023-02-24 10:26:31,543][00397] Fps is (10 sec: 3276.2, 60 sec: 2047.9, 300 sec: 2047.9). Total num frames: 61440. Throughput: 0: 504.0. Samples: 15120. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) +[2023-02-24 10:26:31,553][00397] Avg episode reward: [(0, '4.359')] +[2023-02-24 10:26:36,542][00397] Fps is (10 sec: 2457.6, 60 sec: 2106.5, 300 sec: 2106.5). Total num frames: 73728. Throughput: 0: 543.5. Samples: 19024. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) +[2023-02-24 10:26:36,544][00397] Avg episode reward: [(0, '4.488')] +[2023-02-24 10:26:37,533][12761] Updated weights for policy 0, policy_version 20 (0.0017) +[2023-02-24 10:26:41,542][00397] Fps is (10 sec: 3687.0, 60 sec: 2457.6, 300 sec: 2457.6). Total num frames: 98304. Throughput: 0: 554.3. Samples: 22170. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:26:41,545][00397] Avg episode reward: [(0, '4.469')] +[2023-02-24 10:26:46,550][00397] Fps is (10 sec: 4501.9, 60 sec: 2639.2, 300 sec: 2639.2). Total num frames: 118784. Throughput: 0: 634.7. Samples: 28568. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:26:46,553][00397] Avg episode reward: [(0, '4.440')] +[2023-02-24 10:26:46,555][12747] Saving new best policy, reward=4.440! +[2023-02-24 10:26:47,689][12761] Updated weights for policy 0, policy_version 30 (0.0024) +[2023-02-24 10:26:51,543][00397] Fps is (10 sec: 3276.4, 60 sec: 2621.4, 300 sec: 2621.4). Total num frames: 131072. Throughput: 0: 737.4. Samples: 33184. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:26:51,546][00397] Avg episode reward: [(0, '4.400')] +[2023-02-24 10:26:56,542][00397] Fps is (10 sec: 2459.6, 60 sec: 2606.6, 300 sec: 2606.6). Total num frames: 143360. Throughput: 0: 770.6. Samples: 35102. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:26:56,544][00397] Avg episode reward: [(0, '4.308')] +[2023-02-24 10:27:00,326][12761] Updated weights for policy 0, policy_version 40 (0.0013) +[2023-02-24 10:27:01,542][00397] Fps is (10 sec: 3686.9, 60 sec: 2798.9, 300 sec: 2798.9). Total num frames: 167936. Throughput: 0: 857.8. Samples: 40746. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:27:01,547][00397] Avg episode reward: [(0, '4.336')] +[2023-02-24 10:27:06,546][00397] Fps is (10 sec: 4094.2, 60 sec: 3071.8, 300 sec: 2835.5). Total num frames: 184320. Throughput: 0: 889.6. Samples: 47056. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:27:06,549][00397] Avg episode reward: [(0, '4.565')] +[2023-02-24 10:27:06,582][12747] Saving new best policy, reward=4.565! +[2023-02-24 10:27:11,543][00397] Fps is (10 sec: 3276.2, 60 sec: 3345.0, 300 sec: 2867.1). Total num frames: 200704. Throughput: 0: 861.2. Samples: 48948. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:27:11,552][00397] Avg episode reward: [(0, '4.570')] +[2023-02-24 10:27:11,568][12747] Saving new best policy, reward=4.570! +[2023-02-24 10:27:12,965][12761] Updated weights for policy 0, policy_version 50 (0.0037) +[2023-02-24 10:27:16,542][00397] Fps is (10 sec: 2868.4, 60 sec: 3481.6, 300 sec: 2839.9). Total num frames: 212992. Throughput: 0: 840.6. Samples: 52946. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:27:16,544][00397] Avg episode reward: [(0, '4.430')] +[2023-02-24 10:27:21,542][00397] Fps is (10 sec: 3687.1, 60 sec: 3481.6, 300 sec: 2969.6). Total num frames: 237568. Throughput: 0: 890.4. Samples: 59092. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:27:21,549][00397] Avg episode reward: [(0, '4.445')] +[2023-02-24 10:27:23,339][12761] Updated weights for policy 0, policy_version 60 (0.0031) +[2023-02-24 10:27:26,543][00397] Fps is (10 sec: 4095.5, 60 sec: 3413.3, 300 sec: 2987.6). Total num frames: 253952. Throughput: 0: 890.6. Samples: 62248. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:27:26,546][00397] Avg episode reward: [(0, '4.562')] +[2023-02-24 10:27:31,549][00397] Fps is (10 sec: 3274.4, 60 sec: 3481.3, 300 sec: 3003.5). Total num frames: 270336. Throughput: 0: 850.5. Samples: 66838. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:27:31,552][00397] Avg episode reward: [(0, '4.560')] +[2023-02-24 10:27:31,562][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000066_270336.pth... +[2023-02-24 10:27:36,557][00397] Fps is (10 sec: 2863.2, 60 sec: 3480.7, 300 sec: 2974.5). Total num frames: 282624. Throughput: 0: 845.7. Samples: 71252. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:27:36,563][00397] Avg episode reward: [(0, '4.396')] +[2023-02-24 10:27:36,626][12761] Updated weights for policy 0, policy_version 70 (0.0020) +[2023-02-24 10:27:41,542][00397] Fps is (10 sec: 3689.1, 60 sec: 3481.6, 300 sec: 3072.0). Total num frames: 307200. Throughput: 0: 876.0. Samples: 74524. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:27:41,544][00397] Avg episode reward: [(0, '4.420')] +[2023-02-24 10:27:46,542][00397] Fps is (10 sec: 4102.3, 60 sec: 3413.8, 300 sec: 3081.8). Total num frames: 323584. Throughput: 0: 893.7. Samples: 80962. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:27:46,547][00397] Avg episode reward: [(0, '4.498')] +[2023-02-24 10:27:46,761][12761] Updated weights for policy 0, policy_version 80 (0.0013) +[2023-02-24 10:27:51,547][00397] Fps is (10 sec: 3275.1, 60 sec: 3481.4, 300 sec: 3090.5). Total num frames: 339968. Throughput: 0: 843.9. Samples: 85034. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:27:51,549][00397] Avg episode reward: [(0, '4.251')] +[2023-02-24 10:27:56,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3098.7). Total num frames: 356352. Throughput: 0: 846.3. Samples: 87030. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:27:56,545][00397] Avg episode reward: [(0, '4.144')] +[2023-02-24 10:27:59,421][12761] Updated weights for policy 0, policy_version 90 (0.0013) +[2023-02-24 10:28:01,542][00397] Fps is (10 sec: 3688.3, 60 sec: 3481.6, 300 sec: 3140.3). Total num frames: 376832. Throughput: 0: 887.2. Samples: 92868. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:28:01,550][00397] Avg episode reward: [(0, '4.316')] +[2023-02-24 10:28:06,546][00397] Fps is (10 sec: 3684.8, 60 sec: 3481.6, 300 sec: 3145.6). Total num frames: 393216. Throughput: 0: 886.2. Samples: 98976. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:28:06,549][00397] Avg episode reward: [(0, '4.524')] +[2023-02-24 10:28:11,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3413.4, 300 sec: 3119.3). Total num frames: 405504. Throughput: 0: 858.3. Samples: 100872. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:28:11,546][00397] Avg episode reward: [(0, '4.503')] +[2023-02-24 10:28:11,582][12761] Updated weights for policy 0, policy_version 100 (0.0024) +[2023-02-24 10:28:16,542][00397] Fps is (10 sec: 2868.4, 60 sec: 3481.6, 300 sec: 3125.1). Total num frames: 421888. Throughput: 0: 847.0. Samples: 104946. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:28:16,544][00397] Avg episode reward: [(0, '4.497')] +[2023-02-24 10:28:21,547][00397] Fps is (10 sec: 4093.7, 60 sec: 3481.3, 300 sec: 3188.9). Total num frames: 446464. Throughput: 0: 889.7. Samples: 111280. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:28:21,550][00397] Avg episode reward: [(0, '4.344')] +[2023-02-24 10:28:22,526][12761] Updated weights for policy 0, policy_version 110 (0.0017) +[2023-02-24 10:28:26,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3481.7, 300 sec: 3192.1). Total num frames: 462848. Throughput: 0: 886.8. Samples: 114430. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:28:26,547][00397] Avg episode reward: [(0, '4.426')] +[2023-02-24 10:28:31,542][00397] Fps is (10 sec: 2868.8, 60 sec: 3413.8, 300 sec: 3167.6). Total num frames: 475136. Throughput: 0: 840.5. Samples: 118786. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:28:31,548][00397] Avg episode reward: [(0, '4.379')] +[2023-02-24 10:28:36,134][12761] Updated weights for policy 0, policy_version 120 (0.0016) +[2023-02-24 10:28:36,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3482.5, 300 sec: 3171.1). Total num frames: 491520. Throughput: 0: 846.2. Samples: 123108. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 10:28:36,551][00397] Avg episode reward: [(0, '4.322')] +[2023-02-24 10:28:41,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3276.8, 300 sec: 3148.8). Total num frames: 503808. Throughput: 0: 846.0. Samples: 125100. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:28:41,546][00397] Avg episode reward: [(0, '4.458')] +[2023-02-24 10:28:46,543][00397] Fps is (10 sec: 2457.4, 60 sec: 3208.5, 300 sec: 3127.8). Total num frames: 516096. Throughput: 0: 806.4. Samples: 129156. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:28:46,548][00397] Avg episode reward: [(0, '4.490')] +[2023-02-24 10:28:51,542][00397] Fps is (10 sec: 2457.5, 60 sec: 3140.5, 300 sec: 3108.1). Total num frames: 528384. Throughput: 0: 756.6. Samples: 133020. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:28:51,546][00397] Avg episode reward: [(0, '4.598')] +[2023-02-24 10:28:51,571][12761] Updated weights for policy 0, policy_version 130 (0.0029) +[2023-02-24 10:28:51,574][12747] Saving new best policy, reward=4.598! +[2023-02-24 10:28:56,542][00397] Fps is (10 sec: 3277.2, 60 sec: 3208.5, 300 sec: 3136.4). Total num frames: 548864. Throughput: 0: 756.5. Samples: 134916. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:28:56,549][00397] Avg episode reward: [(0, '4.536')] +[2023-02-24 10:29:01,542][00397] Fps is (10 sec: 4096.1, 60 sec: 3208.5, 300 sec: 3163.0). Total num frames: 569344. Throughput: 0: 807.4. Samples: 141280. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:29:01,547][00397] Avg episode reward: [(0, '4.551')] +[2023-02-24 10:29:02,254][12761] Updated weights for policy 0, policy_version 140 (0.0014) +[2023-02-24 10:29:06,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3208.8, 300 sec: 3166.1). Total num frames: 585728. Throughput: 0: 793.2. Samples: 146970. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:29:06,548][00397] Avg episode reward: [(0, '4.605')] +[2023-02-24 10:29:06,553][12747] Saving new best policy, reward=4.605! +[2023-02-24 10:29:11,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3208.5, 300 sec: 3147.5). Total num frames: 598016. Throughput: 0: 765.6. Samples: 148882. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:29:11,551][00397] Avg episode reward: [(0, '4.576')] +[2023-02-24 10:29:15,844][12761] Updated weights for policy 0, policy_version 150 (0.0019) +[2023-02-24 10:29:16,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3208.5, 300 sec: 3150.8). Total num frames: 614400. Throughput: 0: 759.2. Samples: 152950. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:29:16,544][00397] Avg episode reward: [(0, '4.458')] +[2023-02-24 10:29:21,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3208.8, 300 sec: 3194.9). Total num frames: 638976. Throughput: 0: 804.4. Samples: 159306. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:29:21,551][00397] Avg episode reward: [(0, '4.245')] +[2023-02-24 10:29:25,921][12761] Updated weights for policy 0, policy_version 160 (0.0015) +[2023-02-24 10:29:26,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3208.5, 300 sec: 3196.9). Total num frames: 655360. Throughput: 0: 831.9. Samples: 162536. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:29:26,550][00397] Avg episode reward: [(0, '4.534')] +[2023-02-24 10:29:31,544][00397] Fps is (10 sec: 2866.6, 60 sec: 3208.4, 300 sec: 3179.2). Total num frames: 667648. Throughput: 0: 839.1. Samples: 166916. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:29:31,551][00397] Avg episode reward: [(0, '4.596')] +[2023-02-24 10:29:31,566][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000163_667648.pth... +[2023-02-24 10:29:36,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3208.5, 300 sec: 3181.5). Total num frames: 684032. Throughput: 0: 855.6. Samples: 171522. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:29:36,550][00397] Avg episode reward: [(0, '4.746')] +[2023-02-24 10:29:36,557][12747] Saving new best policy, reward=4.746! +[2023-02-24 10:29:38,391][12761] Updated weights for policy 0, policy_version 170 (0.0014) +[2023-02-24 10:29:41,542][00397] Fps is (10 sec: 4096.9, 60 sec: 3413.3, 300 sec: 3220.9). Total num frames: 708608. Throughput: 0: 886.6. Samples: 174814. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:29:41,545][00397] Avg episode reward: [(0, '4.525')] +[2023-02-24 10:29:46,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3481.7, 300 sec: 3222.2). Total num frames: 724992. Throughput: 0: 891.3. Samples: 181388. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:29:46,552][00397] Avg episode reward: [(0, '4.321')] +[2023-02-24 10:29:49,587][12761] Updated weights for policy 0, policy_version 180 (0.0018) +[2023-02-24 10:29:51,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3223.4). Total num frames: 741376. Throughput: 0: 857.0. Samples: 185536. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:29:51,548][00397] Avg episode reward: [(0, '4.437')] +[2023-02-24 10:29:56,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3224.5). Total num frames: 757760. Throughput: 0: 862.2. Samples: 187680. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:29:56,544][00397] Avg episode reward: [(0, '4.576')] +[2023-02-24 10:30:00,526][12761] Updated weights for policy 0, policy_version 190 (0.0022) +[2023-02-24 10:30:01,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3259.7). Total num frames: 782336. Throughput: 0: 915.2. Samples: 194136. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:30:01,544][00397] Avg episode reward: [(0, '4.475')] +[2023-02-24 10:30:06,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3260.1). Total num frames: 798720. Throughput: 0: 902.9. Samples: 199938. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:30:06,546][00397] Avg episode reward: [(0, '4.353')] +[2023-02-24 10:30:11,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3549.9, 300 sec: 3244.0). Total num frames: 811008. Throughput: 0: 876.8. Samples: 201992. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:30:11,551][00397] Avg episode reward: [(0, '4.365')] +[2023-02-24 10:30:13,575][12761] Updated weights for policy 0, policy_version 200 (0.0013) +[2023-02-24 10:30:16,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3549.9, 300 sec: 3244.7). Total num frames: 827392. Throughput: 0: 876.0. Samples: 206336. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:30:16,543][00397] Avg episode reward: [(0, '4.261')] +[2023-02-24 10:30:21,545][00397] Fps is (10 sec: 4094.8, 60 sec: 3549.7, 300 sec: 3276.8). Total num frames: 851968. Throughput: 0: 918.4. Samples: 212854. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:30:21,547][00397] Avg episode reward: [(0, '4.196')] +[2023-02-24 10:30:22,989][12761] Updated weights for policy 0, policy_version 210 (0.0012) +[2023-02-24 10:30:26,544][00397] Fps is (10 sec: 4095.1, 60 sec: 3549.7, 300 sec: 3276.8). Total num frames: 868352. Throughput: 0: 918.9. Samples: 216166. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:30:26,547][00397] Avg episode reward: [(0, '4.383')] +[2023-02-24 10:30:31,542][00397] Fps is (10 sec: 2868.1, 60 sec: 3550.0, 300 sec: 3261.6). Total num frames: 880640. Throughput: 0: 860.3. Samples: 220100. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:30:31,549][00397] Avg episode reward: [(0, '4.563')] +[2023-02-24 10:30:36,284][12761] Updated weights for policy 0, policy_version 220 (0.0018) +[2023-02-24 10:30:36,542][00397] Fps is (10 sec: 3277.5, 60 sec: 3618.1, 300 sec: 3276.8). Total num frames: 901120. Throughput: 0: 879.1. Samples: 225094. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:30:36,544][00397] Avg episode reward: [(0, '4.423')] +[2023-02-24 10:30:41,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3291.4). Total num frames: 921600. Throughput: 0: 900.9. Samples: 228220. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:30:41,544][00397] Avg episode reward: [(0, '4.441')] +[2023-02-24 10:30:46,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3291.2). Total num frames: 937984. Throughput: 0: 885.6. Samples: 233986. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:30:46,548][00397] Avg episode reward: [(0, '4.573')] +[2023-02-24 10:30:47,629][12761] Updated weights for policy 0, policy_version 230 (0.0012) +[2023-02-24 10:30:51,543][00397] Fps is (10 sec: 2866.7, 60 sec: 3481.5, 300 sec: 3276.8). Total num frames: 950272. Throughput: 0: 843.9. Samples: 237916. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:30:51,546][00397] Avg episode reward: [(0, '4.610')] +[2023-02-24 10:30:56,542][00397] Fps is (10 sec: 2867.3, 60 sec: 3481.6, 300 sec: 3276.8). Total num frames: 966656. Throughput: 0: 845.1. Samples: 240022. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:30:56,543][00397] Avg episode reward: [(0, '4.339')] +[2023-02-24 10:30:59,783][12761] Updated weights for policy 0, policy_version 240 (0.0013) +[2023-02-24 10:31:01,542][00397] Fps is (10 sec: 4096.7, 60 sec: 3481.6, 300 sec: 3360.1). Total num frames: 991232. Throughput: 0: 889.5. Samples: 246364. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:31:01,548][00397] Avg episode reward: [(0, '4.437')] +[2023-02-24 10:31:06,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3413.3, 300 sec: 3401.8). Total num frames: 1003520. Throughput: 0: 866.8. Samples: 251858. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:31:06,546][00397] Avg episode reward: [(0, '4.627')] +[2023-02-24 10:31:11,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 1019904. Throughput: 0: 835.9. Samples: 253780. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:31:11,551][00397] Avg episode reward: [(0, '4.617')] +[2023-02-24 10:31:12,876][12761] Updated weights for policy 0, policy_version 250 (0.0019) +[2023-02-24 10:31:16,542][00397] Fps is (10 sec: 3276.7, 60 sec: 3481.6, 300 sec: 3415.6). Total num frames: 1036288. Throughput: 0: 852.1. Samples: 258446. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:31:16,549][00397] Avg episode reward: [(0, '4.685')] +[2023-02-24 10:31:21,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3481.8, 300 sec: 3429.5). Total num frames: 1060864. Throughput: 0: 881.5. Samples: 264762. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:31:21,549][00397] Avg episode reward: [(0, '4.758')] +[2023-02-24 10:31:21,562][12747] Saving new best policy, reward=4.758! +[2023-02-24 10:31:22,579][12761] Updated weights for policy 0, policy_version 260 (0.0017) +[2023-02-24 10:31:26,542][00397] Fps is (10 sec: 3686.5, 60 sec: 3413.5, 300 sec: 3429.6). Total num frames: 1073152. Throughput: 0: 880.0. Samples: 267818. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:31:26,544][00397] Avg episode reward: [(0, '4.728')] +[2023-02-24 10:31:31,542][00397] Fps is (10 sec: 2867.1, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 1089536. Throughput: 0: 842.3. Samples: 271888. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:31:31,547][00397] Avg episode reward: [(0, '4.426')] +[2023-02-24 10:31:31,563][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000266_1089536.pth... +[2023-02-24 10:31:31,720][12747] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000066_270336.pth +[2023-02-24 10:31:35,849][12761] Updated weights for policy 0, policy_version 270 (0.0030) +[2023-02-24 10:31:36,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3413.3, 300 sec: 3415.6). Total num frames: 1105920. Throughput: 0: 864.5. Samples: 276818. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:31:36,549][00397] Avg episode reward: [(0, '4.500')] +[2023-02-24 10:31:41,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3413.3, 300 sec: 3415.7). Total num frames: 1126400. Throughput: 0: 889.2. Samples: 280034. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:31:41,544][00397] Avg episode reward: [(0, '4.643')] +[2023-02-24 10:31:46,438][12761] Updated weights for policy 0, policy_version 280 (0.0017) +[2023-02-24 10:31:46,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 1146880. Throughput: 0: 878.3. Samples: 285888. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:31:46,548][00397] Avg episode reward: [(0, '4.479')] +[2023-02-24 10:31:51,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3481.7, 300 sec: 3443.4). Total num frames: 1159168. Throughput: 0: 850.4. Samples: 290124. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:31:51,544][00397] Avg episode reward: [(0, '4.579')] +[2023-02-24 10:31:56,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3415.6). Total num frames: 1175552. Throughput: 0: 856.6. Samples: 292328. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:31:56,544][00397] Avg episode reward: [(0, '4.613')] +[2023-02-24 10:31:58,543][12761] Updated weights for policy 0, policy_version 290 (0.0017) +[2023-02-24 10:32:01,545][00397] Fps is (10 sec: 4094.6, 60 sec: 3481.4, 300 sec: 3443.4). Total num frames: 1200128. Throughput: 0: 894.4. Samples: 298698. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:32:01,551][00397] Avg episode reward: [(0, '4.516')] +[2023-02-24 10:32:06,547][00397] Fps is (10 sec: 3684.5, 60 sec: 3481.3, 300 sec: 3429.5). Total num frames: 1212416. Throughput: 0: 873.0. Samples: 304050. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:32:06,550][00397] Avg episode reward: [(0, '4.454')] +[2023-02-24 10:32:11,185][12761] Updated weights for policy 0, policy_version 300 (0.0018) +[2023-02-24 10:32:11,542][00397] Fps is (10 sec: 2868.1, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 1228800. Throughput: 0: 848.3. Samples: 305992. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:32:11,545][00397] Avg episode reward: [(0, '4.673')] +[2023-02-24 10:32:16,542][00397] Fps is (10 sec: 2868.7, 60 sec: 3413.3, 300 sec: 3401.8). Total num frames: 1241088. Throughput: 0: 845.0. Samples: 309914. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:32:16,550][00397] Avg episode reward: [(0, '4.879')] +[2023-02-24 10:32:16,556][12747] Saving new best policy, reward=4.879! +[2023-02-24 10:32:21,542][00397] Fps is (10 sec: 2457.6, 60 sec: 3208.5, 300 sec: 3387.9). Total num frames: 1253376. Throughput: 0: 823.9. Samples: 313892. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:32:21,545][00397] Avg episode reward: [(0, '4.866')] +[2023-02-24 10:32:26,304][12761] Updated weights for policy 0, policy_version 310 (0.0043) +[2023-02-24 10:32:26,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3276.8, 300 sec: 3388.0). Total num frames: 1269760. Throughput: 0: 803.8. Samples: 316204. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:32:26,552][00397] Avg episode reward: [(0, '4.879')] +[2023-02-24 10:32:31,543][00397] Fps is (10 sec: 2867.0, 60 sec: 3208.5, 300 sec: 3388.0). Total num frames: 1282048. Throughput: 0: 763.3. Samples: 320238. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:32:31,550][00397] Avg episode reward: [(0, '4.774')] +[2023-02-24 10:32:36,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3276.8, 300 sec: 3374.0). Total num frames: 1302528. Throughput: 0: 782.5. Samples: 325336. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:32:36,544][00397] Avg episode reward: [(0, '4.700')] +[2023-02-24 10:32:38,260][12761] Updated weights for policy 0, policy_version 320 (0.0015) +[2023-02-24 10:32:41,542][00397] Fps is (10 sec: 4096.3, 60 sec: 3276.8, 300 sec: 3387.9). Total num frames: 1323008. Throughput: 0: 806.2. Samples: 328608. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:32:41,544][00397] Avg episode reward: [(0, '4.723')] +[2023-02-24 10:32:46,548][00397] Fps is (10 sec: 3684.1, 60 sec: 3208.2, 300 sec: 3387.9). Total num frames: 1339392. Throughput: 0: 794.1. Samples: 334434. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:32:46,555][00397] Avg episode reward: [(0, '4.640')] +[2023-02-24 10:32:50,666][12761] Updated weights for policy 0, policy_version 330 (0.0026) +[2023-02-24 10:32:51,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3208.5, 300 sec: 3374.0). Total num frames: 1351680. Throughput: 0: 766.2. Samples: 338524. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:32:51,551][00397] Avg episode reward: [(0, '4.509')] +[2023-02-24 10:32:56,542][00397] Fps is (10 sec: 3278.8, 60 sec: 3276.8, 300 sec: 3374.0). Total num frames: 1372160. Throughput: 0: 771.1. Samples: 340692. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:32:56,544][00397] Avg episode reward: [(0, '4.412')] +[2023-02-24 10:33:01,436][12761] Updated weights for policy 0, policy_version 340 (0.0021) +[2023-02-24 10:33:01,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3208.7, 300 sec: 3387.9). Total num frames: 1392640. Throughput: 0: 823.1. Samples: 346954. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:33:01,547][00397] Avg episode reward: [(0, '4.491')] +[2023-02-24 10:33:06,542][00397] Fps is (10 sec: 3686.5, 60 sec: 3277.1, 300 sec: 3401.8). Total num frames: 1409024. Throughput: 0: 851.4. Samples: 352204. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:33:06,548][00397] Avg episode reward: [(0, '4.642')] +[2023-02-24 10:33:11,543][00397] Fps is (10 sec: 2866.9, 60 sec: 3208.5, 300 sec: 3387.9). Total num frames: 1421312. Throughput: 0: 842.2. Samples: 354102. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:33:11,545][00397] Avg episode reward: [(0, '4.627')] +[2023-02-24 10:33:14,471][12761] Updated weights for policy 0, policy_version 350 (0.0026) +[2023-02-24 10:33:16,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3345.1, 300 sec: 3374.1). Total num frames: 1441792. Throughput: 0: 863.7. Samples: 359104. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:33:16,549][00397] Avg episode reward: [(0, '4.632')] +[2023-02-24 10:33:21,542][00397] Fps is (10 sec: 4096.5, 60 sec: 3481.6, 300 sec: 3387.9). Total num frames: 1462272. Throughput: 0: 895.3. Samples: 365626. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:33:21,549][00397] Avg episode reward: [(0, '4.548')] +[2023-02-24 10:33:24,280][12761] Updated weights for policy 0, policy_version 360 (0.0016) +[2023-02-24 10:33:26,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3401.8). Total num frames: 1478656. Throughput: 0: 889.6. Samples: 368642. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:33:26,548][00397] Avg episode reward: [(0, '4.595')] +[2023-02-24 10:33:31,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3387.9). Total num frames: 1490944. Throughput: 0: 852.8. Samples: 372804. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:33:31,544][00397] Avg episode reward: [(0, '4.586')] +[2023-02-24 10:33:31,553][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000364_1490944.pth... +[2023-02-24 10:33:31,774][12747] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000163_667648.pth +[2023-02-24 10:33:36,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3415.6). Total num frames: 1511424. Throughput: 0: 871.5. Samples: 377742. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:33:36,550][00397] Avg episode reward: [(0, '4.606')] +[2023-02-24 10:33:37,318][12761] Updated weights for policy 0, policy_version 370 (0.0011) +[2023-02-24 10:33:41,542][00397] Fps is (10 sec: 4095.9, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 1531904. Throughput: 0: 890.4. Samples: 380762. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:33:41,544][00397] Avg episode reward: [(0, '4.647')] +[2023-02-24 10:33:46,544][00397] Fps is (10 sec: 3685.6, 60 sec: 3481.8, 300 sec: 3457.3). Total num frames: 1548288. Throughput: 0: 877.7. Samples: 386452. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:33:46,548][00397] Avg episode reward: [(0, '4.819')] +[2023-02-24 10:33:49,294][12761] Updated weights for policy 0, policy_version 380 (0.0021) +[2023-02-24 10:33:51,542][00397] Fps is (10 sec: 2867.3, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 1560576. Throughput: 0: 849.9. Samples: 390450. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:33:51,544][00397] Avg episode reward: [(0, '4.937')] +[2023-02-24 10:33:51,570][12747] Saving new best policy, reward=4.937! +[2023-02-24 10:33:56,542][00397] Fps is (10 sec: 3277.5, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 1581056. Throughput: 0: 861.4. Samples: 392866. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:33:56,547][00397] Avg episode reward: [(0, '4.622')] +[2023-02-24 10:34:00,027][12761] Updated weights for policy 0, policy_version 390 (0.0020) +[2023-02-24 10:34:01,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 1601536. Throughput: 0: 898.4. Samples: 399530. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:34:01,544][00397] Avg episode reward: [(0, '4.789')] +[2023-02-24 10:34:06,542][00397] Fps is (10 sec: 3686.2, 60 sec: 3481.6, 300 sec: 3457.3). Total num frames: 1617920. Throughput: 0: 868.7. Samples: 404720. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:34:06,550][00397] Avg episode reward: [(0, '4.960')] +[2023-02-24 10:34:06,553][12747] Saving new best policy, reward=4.960! +[2023-02-24 10:34:11,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3481.7, 300 sec: 3443.4). Total num frames: 1630208. Throughput: 0: 845.6. Samples: 406692. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:34:11,544][00397] Avg episode reward: [(0, '4.873')] +[2023-02-24 10:34:13,241][12761] Updated weights for policy 0, policy_version 400 (0.0020) +[2023-02-24 10:34:16,542][00397] Fps is (10 sec: 3277.0, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 1650688. Throughput: 0: 866.5. Samples: 411798. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:34:16,544][00397] Avg episode reward: [(0, '4.936')] +[2023-02-24 10:34:21,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 1671168. Throughput: 0: 902.3. Samples: 418346. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:34:21,544][00397] Avg episode reward: [(0, '4.999')] +[2023-02-24 10:34:21,581][12747] Saving new best policy, reward=4.999! +[2023-02-24 10:34:22,653][12761] Updated weights for policy 0, policy_version 410 (0.0028) +[2023-02-24 10:34:26,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3457.3). Total num frames: 1687552. Throughput: 0: 893.3. Samples: 420960. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:34:26,549][00397] Avg episode reward: [(0, '4.886')] +[2023-02-24 10:34:31,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 1699840. Throughput: 0: 856.3. Samples: 424984. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:34:31,544][00397] Avg episode reward: [(0, '4.727')] +[2023-02-24 10:34:35,634][12761] Updated weights for policy 0, policy_version 420 (0.0020) +[2023-02-24 10:34:36,542][00397] Fps is (10 sec: 3276.7, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 1720320. Throughput: 0: 890.3. Samples: 430514. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:34:36,551][00397] Avg episode reward: [(0, '5.020')] +[2023-02-24 10:34:36,554][12747] Saving new best policy, reward=5.020! +[2023-02-24 10:34:41,542][00397] Fps is (10 sec: 4505.6, 60 sec: 3549.9, 300 sec: 3457.3). Total num frames: 1744896. Throughput: 0: 911.1. Samples: 433866. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:34:41,544][00397] Avg episode reward: [(0, '5.240')] +[2023-02-24 10:34:41,561][12747] Saving new best policy, reward=5.240! +[2023-02-24 10:34:46,543][00397] Fps is (10 sec: 3686.0, 60 sec: 3481.7, 300 sec: 3443.4). Total num frames: 1757184. Throughput: 0: 881.1. Samples: 439182. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:34:46,546][00397] Avg episode reward: [(0, '5.235')] +[2023-02-24 10:34:47,007][12761] Updated weights for policy 0, policy_version 430 (0.0019) +[2023-02-24 10:34:51,542][00397] Fps is (10 sec: 2867.1, 60 sec: 3549.9, 300 sec: 3443.4). Total num frames: 1773568. Throughput: 0: 856.0. Samples: 443238. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:34:51,544][00397] Avg episode reward: [(0, '5.292')] +[2023-02-24 10:34:51,559][12747] Saving new best policy, reward=5.292! +[2023-02-24 10:34:56,542][00397] Fps is (10 sec: 3277.2, 60 sec: 3481.6, 300 sec: 3415.6). Total num frames: 1789952. Throughput: 0: 872.5. Samples: 445954. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:34:56,549][00397] Avg episode reward: [(0, '5.452')] +[2023-02-24 10:34:56,553][12747] Saving new best policy, reward=5.452! +[2023-02-24 10:34:58,600][12761] Updated weights for policy 0, policy_version 440 (0.0032) +[2023-02-24 10:35:01,542][00397] Fps is (10 sec: 4096.1, 60 sec: 3549.9, 300 sec: 3443.4). Total num frames: 1814528. Throughput: 0: 898.6. Samples: 452234. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:35:01,547][00397] Avg episode reward: [(0, '5.290')] +[2023-02-24 10:35:06,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 1826816. Throughput: 0: 863.3. Samples: 457196. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:35:06,546][00397] Avg episode reward: [(0, '5.209')] +[2023-02-24 10:35:11,542][00397] Fps is (10 sec: 2457.4, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 1839104. Throughput: 0: 850.4. Samples: 459228. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:35:11,552][00397] Avg episode reward: [(0, '5.116')] +[2023-02-24 10:35:11,835][12761] Updated weights for policy 0, policy_version 450 (0.0012) +[2023-02-24 10:35:16,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3415.7). Total num frames: 1859584. Throughput: 0: 878.0. Samples: 464492. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:35:16,544][00397] Avg episode reward: [(0, '5.529')] +[2023-02-24 10:35:16,599][12747] Saving new best policy, reward=5.529! +[2023-02-24 10:35:21,115][12761] Updated weights for policy 0, policy_version 460 (0.0023) +[2023-02-24 10:35:21,542][00397] Fps is (10 sec: 4505.9, 60 sec: 3549.9, 300 sec: 3443.4). Total num frames: 1884160. Throughput: 0: 902.2. Samples: 471114. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:35:21,547][00397] Avg episode reward: [(0, '5.977')] +[2023-02-24 10:35:21,560][12747] Saving new best policy, reward=5.977! +[2023-02-24 10:35:26,546][00397] Fps is (10 sec: 3684.8, 60 sec: 3481.3, 300 sec: 3443.4). Total num frames: 1896448. Throughput: 0: 882.2. Samples: 473570. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:35:26,551][00397] Avg episode reward: [(0, '5.943')] +[2023-02-24 10:35:31,543][00397] Fps is (10 sec: 2866.8, 60 sec: 3549.8, 300 sec: 3429.5). Total num frames: 1912832. Throughput: 0: 854.3. Samples: 477624. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:35:31,546][00397] Avg episode reward: [(0, '6.249')] +[2023-02-24 10:35:31,557][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000467_1912832.pth... +[2023-02-24 10:35:31,671][12747] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000266_1089536.pth +[2023-02-24 10:35:31,684][12747] Saving new best policy, reward=6.249! +[2023-02-24 10:35:34,391][12761] Updated weights for policy 0, policy_version 470 (0.0026) +[2023-02-24 10:35:36,542][00397] Fps is (10 sec: 3688.0, 60 sec: 3549.9, 300 sec: 3429.5). Total num frames: 1933312. Throughput: 0: 885.3. Samples: 483076. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:35:36,549][00397] Avg episode reward: [(0, '6.168')] +[2023-02-24 10:35:41,542][00397] Fps is (10 sec: 4096.5, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 1953792. Throughput: 0: 897.5. Samples: 486342. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:35:41,545][00397] Avg episode reward: [(0, '6.467')] +[2023-02-24 10:35:41,562][12747] Saving new best policy, reward=6.467! +[2023-02-24 10:35:45,342][12761] Updated weights for policy 0, policy_version 480 (0.0017) +[2023-02-24 10:35:46,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3481.7, 300 sec: 3443.4). Total num frames: 1966080. Throughput: 0: 872.9. Samples: 491516. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:35:46,547][00397] Avg episode reward: [(0, '6.520')] +[2023-02-24 10:35:46,552][12747] Saving new best policy, reward=6.520! +[2023-02-24 10:35:51,547][00397] Fps is (10 sec: 2456.3, 60 sec: 3413.0, 300 sec: 3429.5). Total num frames: 1978368. Throughput: 0: 852.8. Samples: 495576. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:35:51,550][00397] Avg episode reward: [(0, '6.268')] +[2023-02-24 10:35:56,543][00397] Fps is (10 sec: 2866.8, 60 sec: 3413.3, 300 sec: 3401.7). Total num frames: 1994752. Throughput: 0: 849.9. Samples: 497472. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:35:56,550][00397] Avg episode reward: [(0, '6.402')] +[2023-02-24 10:36:00,651][12761] Updated weights for policy 0, policy_version 490 (0.0032) +[2023-02-24 10:36:01,542][00397] Fps is (10 sec: 2868.7, 60 sec: 3208.5, 300 sec: 3401.8). Total num frames: 2007040. Throughput: 0: 823.9. Samples: 501566. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:36:01,553][00397] Avg episode reward: [(0, '6.270')] +[2023-02-24 10:36:06,542][00397] Fps is (10 sec: 2867.6, 60 sec: 3276.8, 300 sec: 3401.8). Total num frames: 2023424. Throughput: 0: 779.7. Samples: 506202. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 10:36:06,544][00397] Avg episode reward: [(0, '6.493')] +[2023-02-24 10:36:11,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3276.8, 300 sec: 3387.9). Total num frames: 2035712. Throughput: 0: 769.9. Samples: 508214. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:36:11,547][00397] Avg episode reward: [(0, '6.902')] +[2023-02-24 10:36:11,568][12747] Saving new best policy, reward=6.902! +[2023-02-24 10:36:13,967][12761] Updated weights for policy 0, policy_version 500 (0.0013) +[2023-02-24 10:36:16,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3276.8, 300 sec: 3374.0). Total num frames: 2056192. Throughput: 0: 795.8. Samples: 513436. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:36:16,548][00397] Avg episode reward: [(0, '6.981')] +[2023-02-24 10:36:16,553][12747] Saving new best policy, reward=6.981! +[2023-02-24 10:36:21,542][00397] Fps is (10 sec: 4505.7, 60 sec: 3276.8, 300 sec: 3415.6). Total num frames: 2080768. Throughput: 0: 815.5. Samples: 519772. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:36:21,549][00397] Avg episode reward: [(0, '7.023')] +[2023-02-24 10:36:21,561][12747] Saving new best policy, reward=7.023! +[2023-02-24 10:36:24,480][12761] Updated weights for policy 0, policy_version 510 (0.0023) +[2023-02-24 10:36:26,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3277.0, 300 sec: 3401.8). Total num frames: 2093056. Throughput: 0: 794.9. Samples: 522114. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:36:26,550][00397] Avg episode reward: [(0, '6.686')] +[2023-02-24 10:36:31,542][00397] Fps is (10 sec: 2457.6, 60 sec: 3208.6, 300 sec: 3387.9). Total num frames: 2105344. Throughput: 0: 770.4. Samples: 526184. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:36:31,544][00397] Avg episode reward: [(0, '6.744')] +[2023-02-24 10:36:36,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3208.5, 300 sec: 3387.9). Total num frames: 2125824. Throughput: 0: 807.4. Samples: 531904. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:36:36,544][00397] Avg episode reward: [(0, '6.563')] +[2023-02-24 10:36:36,614][12761] Updated weights for policy 0, policy_version 520 (0.0040) +[2023-02-24 10:36:41,542][00397] Fps is (10 sec: 4505.6, 60 sec: 3276.8, 300 sec: 3401.8). Total num frames: 2150400. Throughput: 0: 837.9. Samples: 535176. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:36:41,551][00397] Avg episode reward: [(0, '7.035')] +[2023-02-24 10:36:41,565][12747] Saving new best policy, reward=7.035! +[2023-02-24 10:36:46,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3276.8, 300 sec: 3401.8). Total num frames: 2162688. Throughput: 0: 862.2. Samples: 540364. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:36:46,549][00397] Avg episode reward: [(0, '7.575')] +[2023-02-24 10:36:46,552][12747] Saving new best policy, reward=7.575! +[2023-02-24 10:36:48,873][12761] Updated weights for policy 0, policy_version 530 (0.0012) +[2023-02-24 10:36:51,542][00397] Fps is (10 sec: 2457.6, 60 sec: 3277.1, 300 sec: 3387.9). Total num frames: 2174976. Throughput: 0: 850.4. Samples: 544470. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:36:51,546][00397] Avg episode reward: [(0, '7.452')] +[2023-02-24 10:36:56,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3413.4, 300 sec: 3387.9). Total num frames: 2199552. Throughput: 0: 871.1. Samples: 547412. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:36:56,549][00397] Avg episode reward: [(0, '8.899')] +[2023-02-24 10:36:56,555][12747] Saving new best policy, reward=8.899! +[2023-02-24 10:36:59,437][12761] Updated weights for policy 0, policy_version 540 (0.0016) +[2023-02-24 10:37:01,549][00397] Fps is (10 sec: 4502.4, 60 sec: 3549.4, 300 sec: 3415.6). Total num frames: 2220032. Throughput: 0: 896.1. Samples: 553768. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:37:01,560][00397] Avg episode reward: [(0, '9.282')] +[2023-02-24 10:37:01,573][12747] Saving new best policy, reward=9.282! +[2023-02-24 10:37:06,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3401.8). Total num frames: 2232320. Throughput: 0: 857.8. Samples: 558374. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:37:06,552][00397] Avg episode reward: [(0, '9.562')] +[2023-02-24 10:37:06,562][12747] Saving new best policy, reward=9.562! +[2023-02-24 10:37:11,542][00397] Fps is (10 sec: 2459.4, 60 sec: 3481.6, 300 sec: 3401.8). Total num frames: 2244608. Throughput: 0: 849.1. Samples: 560324. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:37:11,544][00397] Avg episode reward: [(0, '10.419')] +[2023-02-24 10:37:11,563][12747] Saving new best policy, reward=10.419! +[2023-02-24 10:37:12,924][12761] Updated weights for policy 0, policy_version 550 (0.0018) +[2023-02-24 10:37:16,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 2265088. Throughput: 0: 877.9. Samples: 565688. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:37:16,550][00397] Avg episode reward: [(0, '10.634')] +[2023-02-24 10:37:16,610][12747] Saving new best policy, reward=10.634! +[2023-02-24 10:37:21,542][00397] Fps is (10 sec: 4505.6, 60 sec: 3481.6, 300 sec: 3457.3). Total num frames: 2289664. Throughput: 0: 892.2. Samples: 572054. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:37:21,546][00397] Avg episode reward: [(0, '11.379')] +[2023-02-24 10:37:21,561][12747] Saving new best policy, reward=11.379! +[2023-02-24 10:37:22,975][12761] Updated weights for policy 0, policy_version 560 (0.0019) +[2023-02-24 10:37:26,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3457.3). Total num frames: 2301952. Throughput: 0: 870.7. Samples: 574358. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:37:26,546][00397] Avg episode reward: [(0, '11.885')] +[2023-02-24 10:37:26,552][12747] Saving new best policy, reward=11.885! +[2023-02-24 10:37:31,542][00397] Fps is (10 sec: 2457.6, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 2314240. Throughput: 0: 852.0. Samples: 578706. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:37:31,548][00397] Avg episode reward: [(0, '11.252')] +[2023-02-24 10:37:31,625][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000566_2318336.pth... +[2023-02-24 10:37:31,759][12747] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000364_1490944.pth +[2023-02-24 10:37:35,633][12761] Updated weights for policy 0, policy_version 570 (0.0021) +[2023-02-24 10:37:36,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 2334720. Throughput: 0: 886.0. Samples: 584340. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:37:36,544][00397] Avg episode reward: [(0, '11.801')] +[2023-02-24 10:37:41,542][00397] Fps is (10 sec: 4505.6, 60 sec: 3481.6, 300 sec: 3457.4). Total num frames: 2359296. Throughput: 0: 890.6. Samples: 587488. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:37:41,547][00397] Avg episode reward: [(0, '12.648')] +[2023-02-24 10:37:41,556][12747] Saving new best policy, reward=12.648! +[2023-02-24 10:37:46,545][00397] Fps is (10 sec: 3685.2, 60 sec: 3481.4, 300 sec: 3457.3). Total num frames: 2371584. Throughput: 0: 862.9. Samples: 592596. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:37:46,549][00397] Avg episode reward: [(0, '13.506')] +[2023-02-24 10:37:46,554][12747] Saving new best policy, reward=13.506! +[2023-02-24 10:37:47,558][12761] Updated weights for policy 0, policy_version 580 (0.0032) +[2023-02-24 10:37:51,545][00397] Fps is (10 sec: 2456.8, 60 sec: 3481.4, 300 sec: 3429.5). Total num frames: 2383872. Throughput: 0: 847.7. Samples: 596522. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:37:51,548][00397] Avg episode reward: [(0, '13.849')] +[2023-02-24 10:37:51,556][12747] Saving new best policy, reward=13.849! +[2023-02-24 10:37:56,542][00397] Fps is (10 sec: 3277.9, 60 sec: 3413.3, 300 sec: 3429.5). Total num frames: 2404352. Throughput: 0: 869.9. Samples: 599468. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:37:56,550][00397] Avg episode reward: [(0, '14.924')] +[2023-02-24 10:37:56,555][12747] Saving new best policy, reward=14.924! +[2023-02-24 10:37:58,739][12761] Updated weights for policy 0, policy_version 590 (0.0024) +[2023-02-24 10:38:01,544][00397] Fps is (10 sec: 4096.5, 60 sec: 3413.6, 300 sec: 3443.4). Total num frames: 2424832. Throughput: 0: 890.1. Samples: 605744. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:38:01,547][00397] Avg episode reward: [(0, '14.506')] +[2023-02-24 10:38:06,544][00397] Fps is (10 sec: 3685.5, 60 sec: 3481.5, 300 sec: 3457.3). Total num frames: 2441216. Throughput: 0: 852.1. Samples: 610402. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:38:06,550][00397] Avg episode reward: [(0, '13.757')] +[2023-02-24 10:38:11,542][00397] Fps is (10 sec: 2867.8, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 2453504. Throughput: 0: 845.9. Samples: 612424. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:38:11,547][00397] Avg episode reward: [(0, '13.112')] +[2023-02-24 10:38:11,789][12761] Updated weights for policy 0, policy_version 600 (0.0023) +[2023-02-24 10:38:16,542][00397] Fps is (10 sec: 3687.2, 60 sec: 3549.9, 300 sec: 3443.4). Total num frames: 2478080. Throughput: 0: 876.2. Samples: 618134. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) +[2023-02-24 10:38:16,548][00397] Avg episode reward: [(0, '12.554')] +[2023-02-24 10:38:20,898][12761] Updated weights for policy 0, policy_version 610 (0.0014) +[2023-02-24 10:38:21,542][00397] Fps is (10 sec: 4505.6, 60 sec: 3481.6, 300 sec: 3457.3). Total num frames: 2498560. Throughput: 0: 901.6. Samples: 624912. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:38:21,547][00397] Avg episode reward: [(0, '12.514')] +[2023-02-24 10:38:26,542][00397] Fps is (10 sec: 3686.3, 60 sec: 3549.9, 300 sec: 3471.2). Total num frames: 2514944. Throughput: 0: 884.2. Samples: 627276. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:38:26,549][00397] Avg episode reward: [(0, '13.858')] +[2023-02-24 10:38:31,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3549.9, 300 sec: 3443.4). Total num frames: 2527232. Throughput: 0: 865.8. Samples: 631552. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:38:31,544][00397] Avg episode reward: [(0, '16.050')] +[2023-02-24 10:38:31,560][12747] Saving new best policy, reward=16.050! +[2023-02-24 10:38:33,641][12761] Updated weights for policy 0, policy_version 620 (0.0027) +[2023-02-24 10:38:36,542][00397] Fps is (10 sec: 3686.3, 60 sec: 3618.1, 300 sec: 3457.3). Total num frames: 2551808. Throughput: 0: 913.5. Samples: 637626. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:38:36,554][00397] Avg episode reward: [(0, '16.308')] +[2023-02-24 10:38:36,557][12747] Saving new best policy, reward=16.308! +[2023-02-24 10:38:41,542][00397] Fps is (10 sec: 4505.6, 60 sec: 3549.9, 300 sec: 3471.2). Total num frames: 2572288. Throughput: 0: 920.0. Samples: 640866. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:38:41,544][00397] Avg episode reward: [(0, '16.464')] +[2023-02-24 10:38:41,571][12747] Saving new best policy, reward=16.464! +[2023-02-24 10:38:44,122][12761] Updated weights for policy 0, policy_version 630 (0.0016) +[2023-02-24 10:38:46,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3550.0, 300 sec: 3471.2). Total num frames: 2584576. Throughput: 0: 891.6. Samples: 645864. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:38:46,549][00397] Avg episode reward: [(0, '15.841')] +[2023-02-24 10:38:51,542][00397] Fps is (10 sec: 2457.6, 60 sec: 3550.1, 300 sec: 3443.4). Total num frames: 2596864. Throughput: 0: 877.0. Samples: 649866. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:38:51,544][00397] Avg episode reward: [(0, '15.872')] +[2023-02-24 10:38:56,440][12761] Updated weights for policy 0, policy_version 640 (0.0032) +[2023-02-24 10:38:56,542][00397] Fps is (10 sec: 3686.5, 60 sec: 3618.1, 300 sec: 3457.3). Total num frames: 2621440. Throughput: 0: 902.8. Samples: 653050. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:38:56,549][00397] Avg episode reward: [(0, '16.378')] +[2023-02-24 10:39:01,547][00397] Fps is (10 sec: 4503.2, 60 sec: 3617.9, 300 sec: 3471.1). Total num frames: 2641920. Throughput: 0: 918.4. Samples: 659468. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:39:01,550][00397] Avg episode reward: [(0, '16.883')] +[2023-02-24 10:39:01,564][12747] Saving new best policy, reward=16.883! +[2023-02-24 10:39:06,542][00397] Fps is (10 sec: 3276.7, 60 sec: 3550.0, 300 sec: 3471.2). Total num frames: 2654208. Throughput: 0: 869.9. Samples: 664056. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:39:06,545][00397] Avg episode reward: [(0, '17.849')] +[2023-02-24 10:39:06,551][12747] Saving new best policy, reward=17.849! +[2023-02-24 10:39:08,849][12761] Updated weights for policy 0, policy_version 650 (0.0013) +[2023-02-24 10:39:11,542][00397] Fps is (10 sec: 2868.7, 60 sec: 3618.1, 300 sec: 3457.3). Total num frames: 2670592. Throughput: 0: 860.9. Samples: 666016. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:39:11,545][00397] Avg episode reward: [(0, '17.880')] +[2023-02-24 10:39:11,553][12747] Saving new best policy, reward=17.880! +[2023-02-24 10:39:16,543][00397] Fps is (10 sec: 3276.5, 60 sec: 3481.5, 300 sec: 3443.4). Total num frames: 2686976. Throughput: 0: 881.7. Samples: 671228. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:39:16,550][00397] Avg episode reward: [(0, '17.675')] +[2023-02-24 10:39:19,496][12761] Updated weights for policy 0, policy_version 660 (0.0025) +[2023-02-24 10:39:21,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3471.2). Total num frames: 2711552. Throughput: 0: 889.5. Samples: 677652. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:39:21,549][00397] Avg episode reward: [(0, '17.247')] +[2023-02-24 10:39:26,542][00397] Fps is (10 sec: 3686.7, 60 sec: 3481.6, 300 sec: 3471.2). Total num frames: 2723840. Throughput: 0: 864.9. Samples: 679788. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:39:26,547][00397] Avg episode reward: [(0, '17.597')] +[2023-02-24 10:39:31,542][00397] Fps is (10 sec: 2047.9, 60 sec: 3413.3, 300 sec: 3429.5). Total num frames: 2732032. Throughput: 0: 831.6. Samples: 683288. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:39:31,545][00397] Avg episode reward: [(0, '17.362')] +[2023-02-24 10:39:31,564][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000667_2732032.pth... +[2023-02-24 10:39:31,748][12747] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000467_1912832.pth +[2023-02-24 10:39:35,682][12761] Updated weights for policy 0, policy_version 670 (0.0017) +[2023-02-24 10:39:36,542][00397] Fps is (10 sec: 2048.1, 60 sec: 3208.5, 300 sec: 3387.9). Total num frames: 2744320. Throughput: 0: 821.2. Samples: 686822. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:39:36,545][00397] Avg episode reward: [(0, '18.070')] +[2023-02-24 10:39:36,548][12747] Saving new best policy, reward=18.070! +[2023-02-24 10:39:41,542][00397] Fps is (10 sec: 2867.3, 60 sec: 3140.3, 300 sec: 3401.8). Total num frames: 2760704. Throughput: 0: 793.8. Samples: 688770. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:39:41,544][00397] Avg episode reward: [(0, '18.944')] +[2023-02-24 10:39:41,560][12747] Saving new best policy, reward=18.944! +[2023-02-24 10:39:46,542][00397] Fps is (10 sec: 3276.7, 60 sec: 3208.5, 300 sec: 3401.8). Total num frames: 2777088. Throughput: 0: 767.5. Samples: 694000. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:39:46,546][00397] Avg episode reward: [(0, '19.577')] +[2023-02-24 10:39:46,551][12747] Saving new best policy, reward=19.577! +[2023-02-24 10:39:49,002][12761] Updated weights for policy 0, policy_version 680 (0.0024) +[2023-02-24 10:39:51,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3208.5, 300 sec: 3387.9). Total num frames: 2789376. Throughput: 0: 752.1. Samples: 697898. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:39:51,544][00397] Avg episode reward: [(0, '18.399')] +[2023-02-24 10:39:56,542][00397] Fps is (10 sec: 3276.9, 60 sec: 3140.3, 300 sec: 3374.0). Total num frames: 2809856. Throughput: 0: 765.6. Samples: 700468. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:39:56,549][00397] Avg episode reward: [(0, '18.473')] +[2023-02-24 10:39:59,924][12761] Updated weights for policy 0, policy_version 690 (0.0028) +[2023-02-24 10:40:01,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3140.5, 300 sec: 3401.8). Total num frames: 2830336. Throughput: 0: 790.5. Samples: 706798. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:40:01,549][00397] Avg episode reward: [(0, '18.547')] +[2023-02-24 10:40:06,545][00397] Fps is (10 sec: 3275.7, 60 sec: 3140.1, 300 sec: 3401.7). Total num frames: 2842624. Throughput: 0: 755.5. Samples: 711652. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:40:06,548][00397] Avg episode reward: [(0, '18.487')] +[2023-02-24 10:40:11,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3140.3, 300 sec: 3387.9). Total num frames: 2859008. Throughput: 0: 751.7. Samples: 713614. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:40:11,545][00397] Avg episode reward: [(0, '17.444')] +[2023-02-24 10:40:13,482][12761] Updated weights for policy 0, policy_version 700 (0.0012) +[2023-02-24 10:40:16,542][00397] Fps is (10 sec: 3687.6, 60 sec: 3208.6, 300 sec: 3374.0). Total num frames: 2879488. Throughput: 0: 784.4. Samples: 718586. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:40:16,544][00397] Avg episode reward: [(0, '18.513')] +[2023-02-24 10:40:21,542][00397] Fps is (10 sec: 4095.9, 60 sec: 3140.3, 300 sec: 3401.8). Total num frames: 2899968. Throughput: 0: 850.3. Samples: 725084. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:40:21,544][00397] Avg episode reward: [(0, '18.193')] +[2023-02-24 10:40:23,616][12761] Updated weights for policy 0, policy_version 710 (0.0023) +[2023-02-24 10:40:26,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3140.3, 300 sec: 3387.9). Total num frames: 2912256. Throughput: 0: 861.3. Samples: 727530. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:40:26,549][00397] Avg episode reward: [(0, '17.958')] +[2023-02-24 10:40:31,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3276.8, 300 sec: 3374.0). Total num frames: 2928640. Throughput: 0: 834.0. Samples: 731528. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:40:31,544][00397] Avg episode reward: [(0, '18.287')] +[2023-02-24 10:40:36,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3345.1, 300 sec: 3360.1). Total num frames: 2945024. Throughput: 0: 867.0. Samples: 736912. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:40:36,544][00397] Avg episode reward: [(0, '18.157')] +[2023-02-24 10:40:36,557][12761] Updated weights for policy 0, policy_version 720 (0.0019) +[2023-02-24 10:40:41,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3481.6, 300 sec: 3401.8). Total num frames: 2969600. Throughput: 0: 880.4. Samples: 740086. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:40:41,547][00397] Avg episode reward: [(0, '18.209')] +[2023-02-24 10:40:46,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3413.4, 300 sec: 3401.8). Total num frames: 2981888. Throughput: 0: 856.4. Samples: 745334. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:40:46,550][00397] Avg episode reward: [(0, '18.556')] +[2023-02-24 10:40:48,971][12761] Updated weights for policy 0, policy_version 730 (0.0018) +[2023-02-24 10:40:51,542][00397] Fps is (10 sec: 2457.6, 60 sec: 3413.3, 300 sec: 3387.9). Total num frames: 2994176. Throughput: 0: 835.4. Samples: 749240. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:40:51,544][00397] Avg episode reward: [(0, '18.760')] +[2023-02-24 10:40:56,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3413.3, 300 sec: 3415.6). Total num frames: 3014656. Throughput: 0: 852.3. Samples: 751966. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:40:56,544][00397] Avg episode reward: [(0, '19.041')] +[2023-02-24 10:40:59,702][12761] Updated weights for policy 0, policy_version 740 (0.0021) +[2023-02-24 10:41:01,542][00397] Fps is (10 sec: 4505.6, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 3039232. Throughput: 0: 884.2. Samples: 758376. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 10:41:01,551][00397] Avg episode reward: [(0, '19.427')] +[2023-02-24 10:41:06,543][00397] Fps is (10 sec: 3685.8, 60 sec: 3481.7, 300 sec: 3443.4). Total num frames: 3051520. Throughput: 0: 850.7. Samples: 763368. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:41:06,545][00397] Avg episode reward: [(0, '20.028')] +[2023-02-24 10:41:06,553][12747] Saving new best policy, reward=20.028! +[2023-02-24 10:41:11,542][00397] Fps is (10 sec: 2457.6, 60 sec: 3413.3, 300 sec: 3415.6). Total num frames: 3063808. Throughput: 0: 838.2. Samples: 765248. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 10:41:11,544][00397] Avg episode reward: [(0, '20.465')] +[2023-02-24 10:41:11,554][12747] Saving new best policy, reward=20.465! +[2023-02-24 10:41:12,918][12761] Updated weights for policy 0, policy_version 750 (0.0020) +[2023-02-24 10:41:16,542][00397] Fps is (10 sec: 3277.3, 60 sec: 3413.3, 300 sec: 3401.8). Total num frames: 3084288. Throughput: 0: 865.6. Samples: 770478. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:41:16,544][00397] Avg episode reward: [(0, '20.317')] +[2023-02-24 10:41:21,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3413.3, 300 sec: 3429.5). Total num frames: 3104768. Throughput: 0: 886.7. Samples: 776812. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:41:21,544][00397] Avg episode reward: [(0, '20.717')] +[2023-02-24 10:41:21,557][12747] Saving new best policy, reward=20.717! +[2023-02-24 10:41:23,387][12761] Updated weights for policy 0, policy_version 760 (0.0012) +[2023-02-24 10:41:26,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 3121152. Throughput: 0: 868.0. Samples: 779144. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:41:26,549][00397] Avg episode reward: [(0, '22.091')] +[2023-02-24 10:41:26,561][12747] Saving new best policy, reward=22.091! +[2023-02-24 10:41:31,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3413.3, 300 sec: 3415.6). Total num frames: 3133440. Throughput: 0: 840.9. Samples: 783174. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:41:31,552][00397] Avg episode reward: [(0, '21.025')] +[2023-02-24 10:41:31,562][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000765_3133440.pth... +[2023-02-24 10:41:31,679][12747] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000566_2318336.pth +[2023-02-24 10:41:35,957][12761] Updated weights for policy 0, policy_version 770 (0.0044) +[2023-02-24 10:41:36,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3401.8). Total num frames: 3153920. Throughput: 0: 881.7. Samples: 788916. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 10:41:36,545][00397] Avg episode reward: [(0, '21.090')] +[2023-02-24 10:41:41,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3413.3, 300 sec: 3429.5). Total num frames: 3174400. Throughput: 0: 891.4. Samples: 792080. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 10:41:41,547][00397] Avg episode reward: [(0, '21.272')] +[2023-02-24 10:41:46,543][00397] Fps is (10 sec: 3685.9, 60 sec: 3481.5, 300 sec: 3443.4). Total num frames: 3190784. Throughput: 0: 862.7. Samples: 797200. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:41:46,547][00397] Avg episode reward: [(0, '21.030')] +[2023-02-24 10:41:47,715][12761] Updated weights for policy 0, policy_version 780 (0.0018) +[2023-02-24 10:41:51,544][00397] Fps is (10 sec: 2866.6, 60 sec: 3481.5, 300 sec: 3401.7). Total num frames: 3203072. Throughput: 0: 842.8. Samples: 801294. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:41:51,552][00397] Avg episode reward: [(0, '22.160')] +[2023-02-24 10:41:51,568][12747] Saving new best policy, reward=22.160! +[2023-02-24 10:41:56,542][00397] Fps is (10 sec: 3277.2, 60 sec: 3481.6, 300 sec: 3401.8). Total num frames: 3223552. Throughput: 0: 867.3. Samples: 804276. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:41:56,549][00397] Avg episode reward: [(0, '21.076')] +[2023-02-24 10:41:58,455][12761] Updated weights for policy 0, policy_version 790 (0.0014) +[2023-02-24 10:42:01,544][00397] Fps is (10 sec: 4505.6, 60 sec: 3481.5, 300 sec: 3443.4). Total num frames: 3248128. Throughput: 0: 895.4. Samples: 810774. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:42:01,547][00397] Avg episode reward: [(0, '21.135')] +[2023-02-24 10:42:06,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3481.7, 300 sec: 3443.4). Total num frames: 3260416. Throughput: 0: 855.8. Samples: 815324. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:42:06,544][00397] Avg episode reward: [(0, '20.155')] +[2023-02-24 10:42:11,542][00397] Fps is (10 sec: 2458.1, 60 sec: 3481.6, 300 sec: 3415.6). Total num frames: 3272704. Throughput: 0: 847.6. Samples: 817284. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:42:11,549][00397] Avg episode reward: [(0, '20.994')] +[2023-02-24 10:42:11,862][12761] Updated weights for policy 0, policy_version 800 (0.0026) +[2023-02-24 10:42:16,542][00397] Fps is (10 sec: 3276.7, 60 sec: 3481.6, 300 sec: 3401.8). Total num frames: 3293184. Throughput: 0: 880.6. Samples: 822800. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:42:16,549][00397] Avg episode reward: [(0, '19.746')] +[2023-02-24 10:42:21,542][00397] Fps is (10 sec: 4096.1, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 3313664. Throughput: 0: 894.1. Samples: 829152. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:42:21,547][00397] Avg episode reward: [(0, '19.621')] +[2023-02-24 10:42:21,637][12761] Updated weights for policy 0, policy_version 810 (0.0020) +[2023-02-24 10:42:26,543][00397] Fps is (10 sec: 3686.1, 60 sec: 3481.5, 300 sec: 3443.4). Total num frames: 3330048. Throughput: 0: 874.7. Samples: 831442. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:42:26,547][00397] Avg episode reward: [(0, '19.762')] +[2023-02-24 10:42:31,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3429.5). Total num frames: 3346432. Throughput: 0: 854.6. Samples: 835658. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:42:31,552][00397] Avg episode reward: [(0, '20.909')] +[2023-02-24 10:42:34,417][12761] Updated weights for policy 0, policy_version 820 (0.0017) +[2023-02-24 10:42:36,542][00397] Fps is (10 sec: 3686.8, 60 sec: 3549.9, 300 sec: 3415.6). Total num frames: 3366912. Throughput: 0: 897.3. Samples: 841670. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:42:36,544][00397] Avg episode reward: [(0, '20.931')] +[2023-02-24 10:42:41,544][00397] Fps is (10 sec: 4095.1, 60 sec: 3549.7, 300 sec: 3443.4). Total num frames: 3387392. Throughput: 0: 903.2. Samples: 844920. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:42:41,547][00397] Avg episode reward: [(0, '20.491')] +[2023-02-24 10:42:45,634][12761] Updated weights for policy 0, policy_version 830 (0.0023) +[2023-02-24 10:42:46,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3481.7, 300 sec: 3443.5). Total num frames: 3399680. Throughput: 0: 869.6. Samples: 849902. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:42:46,544][00397] Avg episode reward: [(0, '21.798')] +[2023-02-24 10:42:51,542][00397] Fps is (10 sec: 2867.8, 60 sec: 3550.0, 300 sec: 3429.5). Total num frames: 3416064. Throughput: 0: 859.1. Samples: 853984. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:42:51,549][00397] Avg episode reward: [(0, '20.931')] +[2023-02-24 10:42:56,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3429.6). Total num frames: 3436544. Throughput: 0: 885.0. Samples: 857110. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:42:56,544][00397] Avg episode reward: [(0, '21.026')] +[2023-02-24 10:42:57,144][12761] Updated weights for policy 0, policy_version 840 (0.0021) +[2023-02-24 10:43:01,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3481.7, 300 sec: 3443.4). Total num frames: 3457024. Throughput: 0: 904.9. Samples: 863518. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:43:01,546][00397] Avg episode reward: [(0, '20.184')] +[2023-02-24 10:43:06,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 3469312. Throughput: 0: 859.9. Samples: 867848. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:43:06,544][00397] Avg episode reward: [(0, '19.842')] +[2023-02-24 10:43:11,461][12761] Updated weights for policy 0, policy_version 850 (0.0028) +[2023-02-24 10:43:11,543][00397] Fps is (10 sec: 2457.3, 60 sec: 3481.5, 300 sec: 3401.7). Total num frames: 3481600. Throughput: 0: 845.6. Samples: 869494. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:43:11,546][00397] Avg episode reward: [(0, '19.154')] +[2023-02-24 10:43:16,543][00397] Fps is (10 sec: 2457.4, 60 sec: 3345.0, 300 sec: 3374.0). Total num frames: 3493888. Throughput: 0: 823.7. Samples: 872724. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:43:16,547][00397] Avg episode reward: [(0, '18.908')] +[2023-02-24 10:43:21,542][00397] Fps is (10 sec: 3277.2, 60 sec: 3345.1, 300 sec: 3387.9). Total num frames: 3514368. Throughput: 0: 813.3. Samples: 878268. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:43:21,549][00397] Avg episode reward: [(0, '18.639')] +[2023-02-24 10:43:23,395][12761] Updated weights for policy 0, policy_version 860 (0.0030) +[2023-02-24 10:43:26,542][00397] Fps is (10 sec: 3686.8, 60 sec: 3345.1, 300 sec: 3401.8). Total num frames: 3530752. Throughput: 0: 812.9. Samples: 881500. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:43:26,555][00397] Avg episode reward: [(0, '18.780')] +[2023-02-24 10:43:31,543][00397] Fps is (10 sec: 2866.8, 60 sec: 3276.7, 300 sec: 3360.1). Total num frames: 3543040. Throughput: 0: 798.4. Samples: 885830. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:43:31,548][00397] Avg episode reward: [(0, '20.090')] +[2023-02-24 10:43:31,566][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000865_3543040.pth... +[2023-02-24 10:43:31,723][12747] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000667_2732032.pth +[2023-02-24 10:43:36,362][12761] Updated weights for policy 0, policy_version 870 (0.0038) +[2023-02-24 10:43:36,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3276.8, 300 sec: 3360.1). Total num frames: 3563520. Throughput: 0: 812.6. Samples: 890550. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:43:36,544][00397] Avg episode reward: [(0, '21.596')] +[2023-02-24 10:43:41,542][00397] Fps is (10 sec: 4096.5, 60 sec: 3276.9, 300 sec: 3387.9). Total num frames: 3584000. Throughput: 0: 812.8. Samples: 893688. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:43:41,547][00397] Avg episode reward: [(0, '21.124')] +[2023-02-24 10:43:46,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3345.1, 300 sec: 3401.8). Total num frames: 3600384. Throughput: 0: 809.0. Samples: 899922. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:43:46,544][00397] Avg episode reward: [(0, '22.416')] +[2023-02-24 10:43:46,549][12747] Saving new best policy, reward=22.416! +[2023-02-24 10:43:47,363][12761] Updated weights for policy 0, policy_version 880 (0.0024) +[2023-02-24 10:43:51,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3276.8, 300 sec: 3360.1). Total num frames: 3612672. Throughput: 0: 803.2. Samples: 903994. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:43:51,546][00397] Avg episode reward: [(0, '24.392')] +[2023-02-24 10:43:51,557][12747] Saving new best policy, reward=24.392! +[2023-02-24 10:43:56,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3276.8, 300 sec: 3360.2). Total num frames: 3633152. Throughput: 0: 813.8. Samples: 906116. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:43:56,544][00397] Avg episode reward: [(0, '23.679')] +[2023-02-24 10:43:59,095][12761] Updated weights for policy 0, policy_version 890 (0.0030) +[2023-02-24 10:44:01,542][00397] Fps is (10 sec: 4096.1, 60 sec: 3276.8, 300 sec: 3387.9). Total num frames: 3653632. Throughput: 0: 881.0. Samples: 912370. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:44:01,548][00397] Avg episode reward: [(0, '22.403')] +[2023-02-24 10:44:06,542][00397] Fps is (10 sec: 3686.3, 60 sec: 3345.1, 300 sec: 3387.9). Total num frames: 3670016. Throughput: 0: 884.8. Samples: 918086. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 10:44:06,546][00397] Avg episode reward: [(0, '21.287')] +[2023-02-24 10:44:11,543][00397] Fps is (10 sec: 2866.8, 60 sec: 3345.1, 300 sec: 3374.0). Total num frames: 3682304. Throughput: 0: 856.8. Samples: 920058. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:44:11,546][00397] Avg episode reward: [(0, '20.960')] +[2023-02-24 10:44:11,575][12761] Updated weights for policy 0, policy_version 900 (0.0015) +[2023-02-24 10:44:16,542][00397] Fps is (10 sec: 3276.9, 60 sec: 3481.7, 300 sec: 3360.1). Total num frames: 3702784. Throughput: 0: 859.9. Samples: 924526. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:44:16,550][00397] Avg episode reward: [(0, '19.070')] +[2023-02-24 10:44:21,542][00397] Fps is (10 sec: 4096.6, 60 sec: 3481.6, 300 sec: 3387.9). Total num frames: 3723264. Throughput: 0: 895.6. Samples: 930852. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:44:21,548][00397] Avg episode reward: [(0, '19.823')] +[2023-02-24 10:44:22,053][12761] Updated weights for policy 0, policy_version 910 (0.0012) +[2023-02-24 10:44:26,542][00397] Fps is (10 sec: 3686.3, 60 sec: 3481.6, 300 sec: 3415.6). Total num frames: 3739648. Throughput: 0: 898.0. Samples: 934098. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 10:44:26,548][00397] Avg episode reward: [(0, '19.563')] +[2023-02-24 10:44:31,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3429.5). Total num frames: 3756032. Throughput: 0: 852.7. Samples: 938294. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:44:31,547][00397] Avg episode reward: [(0, '19.353')] +[2023-02-24 10:44:35,015][12761] Updated weights for policy 0, policy_version 920 (0.0042) +[2023-02-24 10:44:36,542][00397] Fps is (10 sec: 3276.9, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 3772416. Throughput: 0: 874.8. Samples: 943360. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:44:36,544][00397] Avg episode reward: [(0, '19.664')] +[2023-02-24 10:44:41,542][00397] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 3792896. Throughput: 0: 899.3. Samples: 946584. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:44:41,544][00397] Avg episode reward: [(0, '20.194')] +[2023-02-24 10:44:45,212][12761] Updated weights for policy 0, policy_version 930 (0.0012) +[2023-02-24 10:44:46,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3471.2). Total num frames: 3813376. Throughput: 0: 893.2. Samples: 952564. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 10:44:46,549][00397] Avg episode reward: [(0, '20.486')] +[2023-02-24 10:44:51,544][00397] Fps is (10 sec: 3275.9, 60 sec: 3549.7, 300 sec: 3443.4). Total num frames: 3825664. Throughput: 0: 854.6. Samples: 956544. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:44:51,550][00397] Avg episode reward: [(0, '19.819')] +[2023-02-24 10:44:56,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 3842048. Throughput: 0: 858.4. Samples: 958686. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:44:56,549][00397] Avg episode reward: [(0, '20.291')] +[2023-02-24 10:44:57,695][12761] Updated weights for policy 0, policy_version 940 (0.0029) +[2023-02-24 10:45:01,544][00397] Fps is (10 sec: 4096.2, 60 sec: 3549.7, 300 sec: 3471.2). Total num frames: 3866624. Throughput: 0: 902.4. Samples: 965138. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 10:45:01,546][00397] Avg episode reward: [(0, '21.595')] +[2023-02-24 10:45:06,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3471.2). Total num frames: 3883008. Throughput: 0: 886.3. Samples: 970736. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:45:06,551][00397] Avg episode reward: [(0, '21.612')] +[2023-02-24 10:45:09,554][12761] Updated weights for policy 0, policy_version 950 (0.0028) +[2023-02-24 10:45:11,542][00397] Fps is (10 sec: 2867.8, 60 sec: 3550.0, 300 sec: 3443.4). Total num frames: 3895296. Throughput: 0: 858.8. Samples: 972744. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 10:45:11,550][00397] Avg episode reward: [(0, '21.260')] +[2023-02-24 10:45:16,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 3911680. Throughput: 0: 868.3. Samples: 977366. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) +[2023-02-24 10:45:16,546][00397] Avg episode reward: [(0, '21.973')] +[2023-02-24 10:45:20,391][12761] Updated weights for policy 0, policy_version 960 (0.0012) +[2023-02-24 10:45:21,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3471.2). Total num frames: 3936256. Throughput: 0: 904.6. Samples: 984066. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:45:21,549][00397] Avg episode reward: [(0, '23.475')] +[2023-02-24 10:45:26,542][00397] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3471.2). Total num frames: 3952640. Throughput: 0: 900.6. Samples: 987110. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) +[2023-02-24 10:45:26,547][00397] Avg episode reward: [(0, '23.064')] +[2023-02-24 10:45:31,542][00397] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3457.3). Total num frames: 3964928. Throughput: 0: 860.0. Samples: 991264. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:45:31,551][00397] Avg episode reward: [(0, '22.880')] +[2023-02-24 10:45:31,564][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000968_3964928.pth... +[2023-02-24 10:45:31,720][12747] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000765_3133440.pth +[2023-02-24 10:45:33,452][12761] Updated weights for policy 0, policy_version 970 (0.0024) +[2023-02-24 10:45:36,542][00397] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3443.4). Total num frames: 3985408. Throughput: 0: 885.3. Samples: 996380. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 10:45:36,545][00397] Avg episode reward: [(0, '23.247')] +[2023-02-24 10:45:40,999][12747] Stopping Batcher_0... +[2023-02-24 10:45:41,000][12747] Loop batcher_evt_loop terminating... +[2023-02-24 10:45:41,000][00397] Component Batcher_0 stopped! +[2023-02-24 10:45:41,004][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... +[2023-02-24 10:45:41,040][12761] Weights refcount: 2 0 +[2023-02-24 10:45:41,056][12761] Stopping InferenceWorker_p0-w0... +[2023-02-24 10:45:41,058][12761] Loop inference_proc0-0_evt_loop terminating... +[2023-02-24 10:45:41,057][00397] Component InferenceWorker_p0-w0 stopped! +[2023-02-24 10:45:41,077][12767] Stopping RolloutWorker_w5... +[2023-02-24 10:45:41,077][00397] Component RolloutWorker_w5 stopped! +[2023-02-24 10:45:41,086][00397] Component RolloutWorker_w7 stopped! +[2023-02-24 10:45:41,098][12764] Stopping RolloutWorker_w1... +[2023-02-24 10:45:41,100][00397] Component RolloutWorker_w1 stopped! +[2023-02-24 10:45:41,086][12768] Stopping RolloutWorker_w7... +[2023-02-24 10:45:41,111][12767] Loop rollout_proc5_evt_loop terminating... +[2023-02-24 10:45:41,127][00397] Component RolloutWorker_w4 stopped! +[2023-02-24 10:45:41,099][12764] Loop rollout_proc1_evt_loop terminating... +[2023-02-24 10:45:41,151][12763] Stopping RolloutWorker_w2... +[2023-02-24 10:45:41,152][12763] Loop rollout_proc2_evt_loop terminating... +[2023-02-24 10:45:41,126][12766] Stopping RolloutWorker_w4... +[2023-02-24 10:45:41,153][12766] Loop rollout_proc4_evt_loop terminating... +[2023-02-24 10:45:41,146][00397] Component RolloutWorker_w3 stopped! +[2023-02-24 10:45:41,157][12762] Stopping RolloutWorker_w0... +[2023-02-24 10:45:41,157][00397] Component RolloutWorker_w2 stopped! +[2023-02-24 10:45:41,158][12762] Loop rollout_proc0_evt_loop terminating... +[2023-02-24 10:45:41,160][12765] Stopping RolloutWorker_w3... +[2023-02-24 10:45:41,160][12769] Stopping RolloutWorker_w6... +[2023-02-24 10:45:41,158][00397] Component RolloutWorker_w0 stopped! +[2023-02-24 10:45:41,163][12769] Loop rollout_proc6_evt_loop terminating... +[2023-02-24 10:45:41,162][00397] Component RolloutWorker_w6 stopped! +[2023-02-24 10:45:41,125][12768] Loop rollout_proc7_evt_loop terminating... +[2023-02-24 10:45:41,183][12765] Loop rollout_proc3_evt_loop terminating... +[2023-02-24 10:45:41,249][12747] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000865_3543040.pth +[2023-02-24 10:45:41,268][12747] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... +[2023-02-24 10:45:41,474][00397] Component LearnerWorker_p0 stopped! +[2023-02-24 10:45:41,481][00397] Waiting for process learner_proc0 to stop... +[2023-02-24 10:45:41,488][12747] Stopping LearnerWorker_p0... +[2023-02-24 10:45:41,488][12747] Loop learner_proc0_evt_loop terminating... +[2023-02-24 10:45:43,508][00397] Waiting for process inference_proc0-0 to join... +[2023-02-24 10:45:44,057][00397] Waiting for process rollout_proc0 to join... +[2023-02-24 10:45:44,060][00397] Waiting for process rollout_proc1 to join... +[2023-02-24 10:45:44,911][00397] Waiting for process rollout_proc2 to join... +[2023-02-24 10:45:44,915][00397] Waiting for process rollout_proc3 to join... +[2023-02-24 10:45:44,923][00397] Waiting for process rollout_proc4 to join... +[2023-02-24 10:45:44,925][00397] Waiting for process rollout_proc5 to join... +[2023-02-24 10:45:44,926][00397] Waiting for process rollout_proc6 to join... +[2023-02-24 10:45:44,928][00397] Waiting for process rollout_proc7 to join... +[2023-02-24 10:45:44,961][00397] Batcher 0 profile tree view: +batching: 26.5562, releasing_batches: 0.0289 +[2023-02-24 10:45:44,969][00397] InferenceWorker_p0-w0 profile tree view: +wait_policy: 0.0075 + wait_policy_total: 582.7002 +update_model: 8.4019 + weight_update: 0.0013 +one_step: 0.0025 + handle_policy_step: 545.4967 + deserialize: 16.4375, stack: 3.2876, obs_to_device_normalize: 120.2653, forward: 265.9622, send_messages: 27.7392 + prepare_outputs: 84.5947 + to_cpu: 51.1742 +[2023-02-24 10:45:44,971][00397] Learner 0 profile tree view: +misc: 0.0056, prepare_batch: 17.5079 +train: 76.7898 + epoch_init: 0.0135, minibatch_init: 0.0062, losses_postprocess: 0.5833, kl_divergence: 0.7025, after_optimizer: 33.2808 + calculate_losses: 27.2141 + losses_init: 0.0092, forward_head: 1.8706, bptt_initial: 17.9257, tail: 1.0741, advantages_returns: 0.2674, losses: 3.5588 + bptt: 2.1899 + bptt_forward_core: 2.0940 + update: 14.2959 + clip: 1.4149 +[2023-02-24 10:45:44,972][00397] RolloutWorker_w0 profile tree view: +wait_for_trajectories: 0.3440, enqueue_policy_requests: 167.7715, env_step: 877.0880, overhead: 24.8764, complete_rollouts: 7.1423 +save_policy_outputs: 22.0396 + split_output_tensors: 10.6108 +[2023-02-24 10:45:44,977][00397] RolloutWorker_w7 profile tree view: +wait_for_trajectories: 0.3963, enqueue_policy_requests: 169.0908, env_step: 874.9294, overhead: 24.2458, complete_rollouts: 7.5913 +save_policy_outputs: 21.5778 + split_output_tensors: 10.2615 +[2023-02-24 10:45:44,982][00397] Loop Runner_EvtLoop terminating... +[2023-02-24 10:45:44,984][00397] Runner profile tree view: +main_loop: 1209.0462 +[2023-02-24 10:45:44,987][00397] Collected {0: 4005888}, FPS: 3313.3 +[2023-02-24 10:47:12,729][00397] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json +[2023-02-24 10:47:12,732][00397] Overriding arg 'num_workers' with value 1 passed from command line +[2023-02-24 10:47:12,735][00397] Adding new argument 'no_render'=True that is not in the saved config file! +[2023-02-24 10:47:12,739][00397] Adding new argument 'save_video'=True that is not in the saved config file! +[2023-02-24 10:47:12,741][00397] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! +[2023-02-24 10:47:12,743][00397] Adding new argument 'video_name'=None that is not in the saved config file! +[2023-02-24 10:47:12,746][00397] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file! +[2023-02-24 10:47:12,747][00397] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! +[2023-02-24 10:47:12,748][00397] Adding new argument 'push_to_hub'=False that is not in the saved config file! +[2023-02-24 10:47:12,749][00397] Adding new argument 'hf_repository'=None that is not in the saved config file! +[2023-02-24 10:47:12,751][00397] Adding new argument 'policy_index'=0 that is not in the saved config file! +[2023-02-24 10:47:12,754][00397] Adding new argument 'eval_deterministic'=False that is not in the saved config file! +[2023-02-24 10:47:12,755][00397] Adding new argument 'train_script'=None that is not in the saved config file! +[2023-02-24 10:47:12,756][00397] Adding new argument 'enjoy_script'=None that is not in the saved config file! +[2023-02-24 10:47:12,757][00397] Using frameskip 1 and render_action_repeat=4 for evaluation +[2023-02-24 10:47:12,794][00397] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 10:47:12,798][00397] RunningMeanStd input shape: (3, 72, 128) +[2023-02-24 10:47:12,802][00397] RunningMeanStd input shape: (1,) +[2023-02-24 10:47:12,832][00397] ConvEncoder: input_channels=3 +[2023-02-24 10:47:13,675][00397] Conv encoder output size: 512 +[2023-02-24 10:47:13,677][00397] Policy head output size: 512 +[2023-02-24 10:47:16,164][00397] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... +[2023-02-24 10:47:17,453][00397] Num frames 100... +[2023-02-24 10:47:17,575][00397] Num frames 200... +[2023-02-24 10:47:17,684][00397] Num frames 300... +[2023-02-24 10:47:17,805][00397] Num frames 400... +[2023-02-24 10:47:17,914][00397] Avg episode rewards: #0: 8.480, true rewards: #0: 4.480 +[2023-02-24 10:47:17,916][00397] Avg episode reward: 8.480, avg true_objective: 4.480 +[2023-02-24 10:47:17,977][00397] Num frames 500... +[2023-02-24 10:47:18,087][00397] Num frames 600... +[2023-02-24 10:47:18,214][00397] Num frames 700... +[2023-02-24 10:47:18,327][00397] Num frames 800... +[2023-02-24 10:47:18,446][00397] Num frames 900... +[2023-02-24 10:47:18,563][00397] Num frames 1000... +[2023-02-24 10:47:18,718][00397] Avg episode rewards: #0: 9.940, true rewards: #0: 5.440 +[2023-02-24 10:47:18,719][00397] Avg episode reward: 9.940, avg true_objective: 5.440 +[2023-02-24 10:47:18,739][00397] Num frames 1100... +[2023-02-24 10:47:18,851][00397] Num frames 1200... +[2023-02-24 10:47:18,965][00397] Num frames 1300... +[2023-02-24 10:47:19,078][00397] Num frames 1400... +[2023-02-24 10:47:19,194][00397] Num frames 1500... +[2023-02-24 10:47:19,305][00397] Num frames 1600... +[2023-02-24 10:47:19,419][00397] Num frames 1700... +[2023-02-24 10:47:19,533][00397] Num frames 1800... +[2023-02-24 10:47:19,654][00397] Num frames 1900... +[2023-02-24 10:47:19,764][00397] Num frames 2000... +[2023-02-24 10:47:19,873][00397] Num frames 2100... +[2023-02-24 10:47:19,991][00397] Num frames 2200... +[2023-02-24 10:47:20,106][00397] Num frames 2300... +[2023-02-24 10:47:20,225][00397] Num frames 2400... +[2023-02-24 10:47:20,344][00397] Num frames 2500... +[2023-02-24 10:47:20,466][00397] Num frames 2600... +[2023-02-24 10:47:20,580][00397] Num frames 2700... +[2023-02-24 10:47:20,709][00397] Num frames 2800... +[2023-02-24 10:47:20,821][00397] Num frames 2900... +[2023-02-24 10:47:20,945][00397] Num frames 3000... +[2023-02-24 10:47:21,056][00397] Num frames 3100... +[2023-02-24 10:47:21,207][00397] Avg episode rewards: #0: 23.960, true rewards: #0: 10.627 +[2023-02-24 10:47:21,209][00397] Avg episode reward: 23.960, avg true_objective: 10.627 +[2023-02-24 10:47:21,228][00397] Num frames 3200... +[2023-02-24 10:47:21,341][00397] Num frames 3300... +[2023-02-24 10:47:21,452][00397] Num frames 3400... +[2023-02-24 10:47:21,578][00397] Num frames 3500... +[2023-02-24 10:47:21,696][00397] Num frames 3600... +[2023-02-24 10:47:21,809][00397] Num frames 3700... +[2023-02-24 10:47:21,922][00397] Num frames 3800... +[2023-02-24 10:47:22,039][00397] Num frames 3900... +[2023-02-24 10:47:22,157][00397] Num frames 4000... +[2023-02-24 10:47:22,269][00397] Num frames 4100... +[2023-02-24 10:47:22,391][00397] Num frames 4200... +[2023-02-24 10:47:22,510][00397] Num frames 4300... +[2023-02-24 10:47:22,621][00397] Num frames 4400... +[2023-02-24 10:47:22,747][00397] Num frames 4500... +[2023-02-24 10:47:22,858][00397] Num frames 4600... +[2023-02-24 10:47:22,970][00397] Num frames 4700... +[2023-02-24 10:47:23,097][00397] Avg episode rewards: #0: 26.640, true rewards: #0: 11.890 +[2023-02-24 10:47:23,100][00397] Avg episode reward: 26.640, avg true_objective: 11.890 +[2023-02-24 10:47:23,151][00397] Num frames 4800... +[2023-02-24 10:47:23,263][00397] Num frames 4900... +[2023-02-24 10:47:23,379][00397] Num frames 5000... +[2023-02-24 10:47:23,488][00397] Num frames 5100... +[2023-02-24 10:47:23,602][00397] Num frames 5200... +[2023-02-24 10:47:23,742][00397] Num frames 5300... +[2023-02-24 10:47:23,909][00397] Num frames 5400... +[2023-02-24 10:47:24,064][00397] Num frames 5500... +[2023-02-24 10:47:24,220][00397] Num frames 5600... +[2023-02-24 10:47:24,382][00397] Num frames 5700... +[2023-02-24 10:47:24,547][00397] Num frames 5800... +[2023-02-24 10:47:24,707][00397] Num frames 5900... +[2023-02-24 10:47:24,873][00397] Num frames 6000... +[2023-02-24 10:47:25,035][00397] Num frames 6100... +[2023-02-24 10:47:25,194][00397] Num frames 6200... +[2023-02-24 10:47:25,375][00397] Num frames 6300... +[2023-02-24 10:47:25,535][00397] Num frames 6400... +[2023-02-24 10:47:25,702][00397] Num frames 6500... +[2023-02-24 10:47:25,875][00397] Num frames 6600... +[2023-02-24 10:47:26,043][00397] Num frames 6700... +[2023-02-24 10:47:26,208][00397] Num frames 6800... +[2023-02-24 10:47:26,292][00397] Avg episode rewards: #0: 32.232, true rewards: #0: 13.632 +[2023-02-24 10:47:26,294][00397] Avg episode reward: 32.232, avg true_objective: 13.632 +[2023-02-24 10:47:26,432][00397] Num frames 6900... +[2023-02-24 10:47:26,599][00397] Num frames 7000... +[2023-02-24 10:47:26,761][00397] Num frames 7100... +[2023-02-24 10:47:26,937][00397] Num frames 7200... +[2023-02-24 10:47:27,104][00397] Num frames 7300... +[2023-02-24 10:47:27,264][00397] Num frames 7400... +[2023-02-24 10:47:27,382][00397] Num frames 7500... +[2023-02-24 10:47:27,495][00397] Num frames 7600... +[2023-02-24 10:47:27,610][00397] Num frames 7700... +[2023-02-24 10:47:27,720][00397] Num frames 7800... +[2023-02-24 10:47:27,836][00397] Num frames 7900... +[2023-02-24 10:47:27,947][00397] Num frames 8000... +[2023-02-24 10:47:28,058][00397] Num frames 8100... +[2023-02-24 10:47:28,171][00397] Num frames 8200... +[2023-02-24 10:47:28,255][00397] Avg episode rewards: #0: 31.873, true rewards: #0: 13.707 +[2023-02-24 10:47:28,256][00397] Avg episode reward: 31.873, avg true_objective: 13.707 +[2023-02-24 10:47:28,347][00397] Num frames 8300... +[2023-02-24 10:47:28,468][00397] Num frames 8400... +[2023-02-24 10:47:28,579][00397] Num frames 8500... +[2023-02-24 10:47:28,689][00397] Num frames 8600... +[2023-02-24 10:47:28,791][00397] Avg episode rewards: #0: 28.200, true rewards: #0: 12.343 +[2023-02-24 10:47:28,793][00397] Avg episode reward: 28.200, avg true_objective: 12.343 +[2023-02-24 10:47:28,871][00397] Num frames 8700... +[2023-02-24 10:47:28,991][00397] Num frames 8800... +[2023-02-24 10:47:29,104][00397] Num frames 8900... +[2023-02-24 10:47:29,219][00397] Num frames 9000... +[2023-02-24 10:47:29,334][00397] Num frames 9100... +[2023-02-24 10:47:29,452][00397] Num frames 9200... +[2023-02-24 10:47:29,564][00397] Num frames 9300... +[2023-02-24 10:47:29,683][00397] Num frames 9400... +[2023-02-24 10:47:29,796][00397] Num frames 9500... +[2023-02-24 10:47:29,921][00397] Num frames 9600... +[2023-02-24 10:47:29,974][00397] Avg episode rewards: #0: 27.750, true rewards: #0: 12.000 +[2023-02-24 10:47:29,975][00397] Avg episode reward: 27.750, avg true_objective: 12.000 +[2023-02-24 10:47:30,089][00397] Num frames 9700... +[2023-02-24 10:47:30,203][00397] Num frames 9800... +[2023-02-24 10:47:30,316][00397] Num frames 9900... +[2023-02-24 10:47:30,429][00397] Num frames 10000... +[2023-02-24 10:47:30,543][00397] Num frames 10100... +[2023-02-24 10:47:30,647][00397] Avg episode rewards: #0: 25.493, true rewards: #0: 11.271 +[2023-02-24 10:47:30,648][00397] Avg episode reward: 25.493, avg true_objective: 11.271 +[2023-02-24 10:47:30,716][00397] Num frames 10200... +[2023-02-24 10:47:30,834][00397] Num frames 10300... +[2023-02-24 10:47:30,950][00397] Num frames 10400... +[2023-02-24 10:47:31,068][00397] Num frames 10500... +[2023-02-24 10:47:31,180][00397] Num frames 10600... +[2023-02-24 10:47:31,296][00397] Num frames 10700... +[2023-02-24 10:47:31,409][00397] Num frames 10800... +[2023-02-24 10:47:31,520][00397] Num frames 10900... +[2023-02-24 10:47:31,632][00397] Num frames 11000... +[2023-02-24 10:47:31,744][00397] Num frames 11100... +[2023-02-24 10:47:31,853][00397] Num frames 11200... +[2023-02-24 10:47:31,951][00397] Avg episode rewards: #0: 25.132, true rewards: #0: 11.232 +[2023-02-24 10:47:31,954][00397] Avg episode reward: 25.132, avg true_objective: 11.232 +[2023-02-24 10:48:39,599][00397] Replay video saved to /content/train_dir/default_experiment/replay.mp4! +[2023-02-24 11:05:47,665][00397] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json +[2023-02-24 11:05:47,671][00397] Overriding arg 'num_workers' with value 1 passed from command line +[2023-02-24 11:05:47,675][00397] Adding new argument 'no_render'=True that is not in the saved config file! +[2023-02-24 11:05:47,680][00397] Adding new argument 'save_video'=True that is not in the saved config file! +[2023-02-24 11:05:47,684][00397] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! +[2023-02-24 11:05:47,686][00397] Adding new argument 'video_name'=None that is not in the saved config file! +[2023-02-24 11:05:47,693][00397] Adding new argument 'max_num_frames'=100000 that is not in the saved config file! +[2023-02-24 11:05:47,696][00397] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! +[2023-02-24 11:05:47,699][00397] Adding new argument 'push_to_hub'=True that is not in the saved config file! +[2023-02-24 11:05:47,703][00397] Adding new argument 'hf_repository'='eldraco/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file! +[2023-02-24 11:05:47,704][00397] Adding new argument 'policy_index'=0 that is not in the saved config file! +[2023-02-24 11:05:47,707][00397] Adding new argument 'eval_deterministic'=False that is not in the saved config file! +[2023-02-24 11:05:47,710][00397] Adding new argument 'train_script'=None that is not in the saved config file! +[2023-02-24 11:05:47,712][00397] Adding new argument 'enjoy_script'=None that is not in the saved config file! +[2023-02-24 11:05:47,715][00397] Using frameskip 1 and render_action_repeat=4 for evaluation +[2023-02-24 11:05:47,747][00397] RunningMeanStd input shape: (3, 72, 128) +[2023-02-24 11:05:47,752][00397] RunningMeanStd input shape: (1,) +[2023-02-24 11:05:47,776][00397] ConvEncoder: input_channels=3 +[2023-02-24 11:05:47,841][00397] Conv encoder output size: 512 +[2023-02-24 11:05:47,849][00397] Policy head output size: 512 +[2023-02-24 11:05:47,897][00397] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... +[2023-02-24 11:05:48,839][00397] Num frames 100... +[2023-02-24 11:05:49,051][00397] Num frames 200... +[2023-02-24 11:05:49,247][00397] Num frames 300... +[2023-02-24 11:05:49,451][00397] Num frames 400... +[2023-02-24 11:05:49,648][00397] Avg episode rewards: #0: 6.610, true rewards: #0: 4.610 +[2023-02-24 11:05:49,655][00397] Avg episode reward: 6.610, avg true_objective: 4.610 +[2023-02-24 11:05:49,756][00397] Num frames 500... +[2023-02-24 11:05:49,997][00397] Num frames 600... +[2023-02-24 11:05:50,235][00397] Num frames 700... +[2023-02-24 11:05:50,489][00397] Num frames 800... +[2023-02-24 11:05:50,646][00397] Num frames 900... +[2023-02-24 11:05:50,759][00397] Num frames 1000... +[2023-02-24 11:05:50,905][00397] Avg episode rewards: #0: 9.900, true rewards: #0: 5.400 +[2023-02-24 11:05:50,908][00397] Avg episode reward: 9.900, avg true_objective: 5.400 +[2023-02-24 11:05:50,935][00397] Num frames 1100... +[2023-02-24 11:05:51,045][00397] Num frames 1200... +[2023-02-24 11:05:51,160][00397] Num frames 1300... +[2023-02-24 11:05:51,274][00397] Num frames 1400... +[2023-02-24 11:05:51,453][00397] Avg episode rewards: #0: 8.977, true rewards: #0: 4.977 +[2023-02-24 11:05:51,455][00397] Avg episode reward: 8.977, avg true_objective: 4.977 +[2023-02-24 11:05:51,468][00397] Num frames 1500... +[2023-02-24 11:05:51,579][00397] Num frames 1600... +[2023-02-24 11:05:51,694][00397] Num frames 1700... +[2023-02-24 11:05:51,806][00397] Num frames 1800... +[2023-02-24 11:05:51,927][00397] Num frames 1900... +[2023-02-24 11:05:52,041][00397] Num frames 2000... +[2023-02-24 11:05:52,161][00397] Num frames 2100... +[2023-02-24 11:05:52,275][00397] Num frames 2200... +[2023-02-24 11:05:52,405][00397] Num frames 2300... +[2023-02-24 11:05:52,535][00397] Num frames 2400... +[2023-02-24 11:05:52,654][00397] Num frames 2500... +[2023-02-24 11:05:52,767][00397] Num frames 2600... +[2023-02-24 11:05:52,889][00397] Num frames 2700... +[2023-02-24 11:05:53,003][00397] Num frames 2800... +[2023-02-24 11:05:53,116][00397] Num frames 2900... +[2023-02-24 11:05:53,228][00397] Num frames 3000... +[2023-02-24 11:05:53,344][00397] Num frames 3100... +[2023-02-24 11:05:53,466][00397] Num frames 3200... +[2023-02-24 11:05:53,583][00397] Num frames 3300... +[2023-02-24 11:05:53,703][00397] Num frames 3400... +[2023-02-24 11:05:53,815][00397] Num frames 3500... +[2023-02-24 11:05:53,980][00397] Avg episode rewards: #0: 20.982, true rewards: #0: 8.983 +[2023-02-24 11:05:53,982][00397] Avg episode reward: 20.982, avg true_objective: 8.983 +[2023-02-24 11:05:53,993][00397] Num frames 3600... +[2023-02-24 11:05:54,109][00397] Num frames 3700... +[2023-02-24 11:05:54,230][00397] Num frames 3800... +[2023-02-24 11:05:54,347][00397] Num frames 3900... +[2023-02-24 11:05:54,468][00397] Num frames 4000... +[2023-02-24 11:05:54,581][00397] Num frames 4100... +[2023-02-24 11:05:54,698][00397] Num frames 4200... +[2023-02-24 11:05:54,809][00397] Num frames 4300... +[2023-02-24 11:05:54,925][00397] Num frames 4400... +[2023-02-24 11:05:55,054][00397] Num frames 4500... +[2023-02-24 11:05:55,220][00397] Num frames 4600... +[2023-02-24 11:05:55,389][00397] Num frames 4700... +[2023-02-24 11:05:55,555][00397] Num frames 4800... +[2023-02-24 11:05:55,718][00397] Num frames 4900... +[2023-02-24 11:05:55,876][00397] Num frames 5000... +[2023-02-24 11:05:56,050][00397] Num frames 5100... +[2023-02-24 11:05:56,219][00397] Num frames 5200... +[2023-02-24 11:05:56,386][00397] Num frames 5300... +[2023-02-24 11:05:56,552][00397] Num frames 5400... +[2023-02-24 11:05:56,714][00397] Num frames 5500... +[2023-02-24 11:05:56,882][00397] Num frames 5600... +[2023-02-24 11:05:57,095][00397] Avg episode rewards: #0: 27.986, true rewards: #0: 11.386 +[2023-02-24 11:05:57,098][00397] Avg episode reward: 27.986, avg true_objective: 11.386 +[2023-02-24 11:05:57,114][00397] Num frames 5700... +[2023-02-24 11:05:57,292][00397] Num frames 5800... +[2023-02-24 11:05:57,475][00397] Num frames 5900... +[2023-02-24 11:05:57,647][00397] Num frames 6000... +[2023-02-24 11:05:57,810][00397] Num frames 6100... +[2023-02-24 11:05:57,978][00397] Num frames 6200... +[2023-02-24 11:05:58,144][00397] Num frames 6300... +[2023-02-24 11:05:58,316][00397] Num frames 6400... +[2023-02-24 11:05:58,490][00397] Num frames 6500... +[2023-02-24 11:05:58,633][00397] Num frames 6600... +[2023-02-24 11:05:58,749][00397] Num frames 6700... +[2023-02-24 11:05:58,871][00397] Num frames 6800... +[2023-02-24 11:05:58,991][00397] Num frames 6900... +[2023-02-24 11:05:59,109][00397] Num frames 7000... +[2023-02-24 11:05:59,224][00397] Num frames 7100... +[2023-02-24 11:05:59,349][00397] Num frames 7200... +[2023-02-24 11:05:59,468][00397] Num frames 7300... +[2023-02-24 11:05:59,597][00397] Avg episode rewards: #0: 30.095, true rewards: #0: 12.262 +[2023-02-24 11:05:59,599][00397] Avg episode reward: 30.095, avg true_objective: 12.262 +[2023-02-24 11:05:59,651][00397] Num frames 7400... +[2023-02-24 11:05:59,762][00397] Num frames 7500... +[2023-02-24 11:05:59,880][00397] Num frames 7600... +[2023-02-24 11:06:00,008][00397] Num frames 7700... +[2023-02-24 11:06:00,122][00397] Num frames 7800... +[2023-02-24 11:06:00,258][00397] Avg episode rewards: #0: 26.955, true rewards: #0: 11.241 +[2023-02-24 11:06:00,261][00397] Avg episode reward: 26.955, avg true_objective: 11.241 +[2023-02-24 11:06:00,301][00397] Num frames 7900... +[2023-02-24 11:06:00,419][00397] Num frames 8000... +[2023-02-24 11:06:00,531][00397] Num frames 8100... +[2023-02-24 11:06:00,654][00397] Num frames 8200... +[2023-02-24 11:06:00,768][00397] Num frames 8300... +[2023-02-24 11:06:00,889][00397] Num frames 8400... +[2023-02-24 11:06:01,003][00397] Num frames 8500... +[2023-02-24 11:06:01,118][00397] Num frames 8600... +[2023-02-24 11:06:01,240][00397] Num frames 8700... +[2023-02-24 11:06:01,356][00397] Num frames 8800... +[2023-02-24 11:06:01,474][00397] Num frames 8900... +[2023-02-24 11:06:01,588][00397] Num frames 9000... +[2023-02-24 11:06:01,715][00397] Num frames 9100... +[2023-02-24 11:06:01,832][00397] Avg episode rewards: #0: 27.441, true rewards: #0: 11.441 +[2023-02-24 11:06:01,834][00397] Avg episode reward: 27.441, avg true_objective: 11.441 +[2023-02-24 11:06:01,891][00397] Num frames 9200... +[2023-02-24 11:06:02,015][00397] Num frames 9300... +[2023-02-24 11:06:02,134][00397] Num frames 9400... +[2023-02-24 11:06:02,253][00397] Num frames 9500... +[2023-02-24 11:06:02,316][00397] Avg episode rewards: #0: 25.116, true rewards: #0: 10.561 +[2023-02-24 11:06:02,318][00397] Avg episode reward: 25.116, avg true_objective: 10.561 +[2023-02-24 11:06:02,427][00397] Num frames 9600... +[2023-02-24 11:06:02,541][00397] Num frames 9700... +[2023-02-24 11:06:02,660][00397] Num frames 9800... +[2023-02-24 11:06:02,773][00397] Num frames 9900... +[2023-02-24 11:06:02,885][00397] Num frames 10000... +[2023-02-24 11:06:03,003][00397] Num frames 10100... +[2023-02-24 11:06:03,131][00397] Num frames 10200... +[2023-02-24 11:06:03,252][00397] Num frames 10300... +[2023-02-24 11:06:03,367][00397] Num frames 10400... +[2023-02-24 11:06:03,487][00397] Num frames 10500... +[2023-02-24 11:06:03,604][00397] Num frames 10600... +[2023-02-24 11:06:03,730][00397] Num frames 10700... +[2023-02-24 11:06:03,812][00397] Avg episode rewards: #0: 25.321, true rewards: #0: 10.721 +[2023-02-24 11:06:03,814][00397] Avg episode reward: 25.321, avg true_objective: 10.721 +[2023-02-24 11:07:09,092][00397] Replay video saved to /content/train_dir/default_experiment/replay.mp4!