Added trained MountainCar-v0

README.md

@@ -16,7 +16,7 @@ model-index:
       type: MountainCar-v0
     metrics:
     - type: mean_reward
-      value: -200.00 +/- 0.00
+      value: -160.60 +/- 39.21
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLmRxbi5wb2xpY2llc5SMCURRTlBvbGljeZSTlC4=", "__module__": "stable_baselines3.dqn.policies", "__annotations__": "{'q_net': <class 'stable_baselines3.dqn.policies.QNetwork'>, 'q_net_target': <class 'stable_baselines3.dqn.policies.QNetwork'>}", "__doc__": "\n    Policy class with Q-Value Net and target net for DQN\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function DQNPolicy.__init__ at 0x7effd5716a70>", "_build": "<function DQNPolicy._build at 0x7effd5716b00>", "make_q_net": "<function DQNPolicy.make_q_net at 0x7effd5716b90>", "forward": "<function DQNPolicy.forward at 0x7effd5716c20>", "_predict": "<function DQNPolicy._predict at 0x7effd5716cb0>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7effd5716d40>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7effd5716dd0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7effd571a880>"}, "verbose": 0, "policy_kwargs": {}, "num_timesteps": 100000, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1697392373848211472, "learning_rate": 0.001, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAPiHF78AAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwKGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAAGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAANjrTb8j6eQ7lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwKGlIwBQ5R0lFKULg=="}, "_episode_num": 500, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHwGkAAAAAAACMAWyUS8iMAXSUR0CtJ5JAt4A0dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtKDv+4smOdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtKOfBvaUSdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtKZYXGff5dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtKkQ++ueSdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtKvWNm16WdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtK7MF2V3VdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtLGwLux8ldX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtLShttQ9BdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtLeL7XQMQdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtLpk078vVdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtL1KISDh+dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtMA2JrLyMdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtMMKlgtvodX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtMXiaAnUldX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtMfbpNbkfdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtMnL6+FlDdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtMvJpeu3ddX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtM3ZbhWHUdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtM/f4REncdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtNHUHY6GQdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtNPNGNJe3dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtNXRxLkCFdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtNfERjBl+dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtNm752yLRdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtNu1o6CDmdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtN3E9U0emdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtN+9SVGCqdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtOGo9cKPXdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtOOpnxri3dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtOWhC+lCUdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtOedrXUYsdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtOmc0UGmldX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtOuSNwR5DdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtO1/su3+ddX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtO9gdwNsndX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtPFXYtg8bdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtPNCXpnpTdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtPUtdzGPxdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtPce3pfQbdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtPkoBBAv+dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtPsuqvNeMdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtP0KrzXjEdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtP7p/XoTxdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtQDJUPxx2dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtQKrcKw6idX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtQSL6+FlDdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtQZvxH5JsdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtQhNeD3/QdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtQpEep4r0dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtQw1mJ3xGdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtQ4k4m1IAdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtRAPStvGZdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtRIdIGyHEdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtRQezt1IRdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtRYO9FnZkdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtRim1pj+adX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtRtHARChOdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtR38f/3nIdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtSCnndO6/dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtSNpMYdhidX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtSY17Qb++dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtSkYLLIPtdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtSwFVDKHPdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtS8GQjlgddX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtTHgoG6f8dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtTTCY9gWrdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtTeqdH2AYdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtTp73XZoPdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtT1PCEYfodX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtUBCP6sQvdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtUJP5HmRvdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtURfj81n/dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtUZJd0JWvdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtUgUVBUrDdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtUn4Ju2qldX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtUwBHbypadX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtU3zErGzbdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtU/fvWpZPdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtVHG5DqnndX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtVPaij+JhdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtVXhc7hegdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtVfXh4t6HdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtVm3t8eCDdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtVvI3zcyndX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtV2/dIoVmdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtV+v2Xb/PdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtWGV45cTrdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtWOMqz7djdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtWWW1lXijdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtWeG29crzdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtWl4AsCkodX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtWt1vVEuydX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtW1+umrKedX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtW9zOoo/idX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtXFoNutOmdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtXNa+vhZRdX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtXVEmplz2dX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtXcyflIVedX2UKGgGR8BpAAAAAAAAaAdLyGgIR0CtXkAWBSUDdWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 12500, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True]", "bounded_above": "[ True  True]", "_shape": [2], "low": "[-1.2  -0.07]", "high": "[0.6  0.07]", "low_repr": "[-1.2  -0.07]", "high_repr": "[0.6  0.07]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWVgAEAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIAwAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlIwUbnVtcHkucmFuZG9tLl9waWNrbGWUjBBfX2dlbmVyYXRvcl9jdG9ylJOUjAVQQ0c2NJSFlFKUfZQojA1iaXRfZ2VuZXJhdG9ylIwFUENHNjSUjAVzdGF0ZZR9lChoI4oQ2h+AHp8rpLEKB4aQYwtDRowDaW5jlIoQXXiWP3f1cEooPx4e8QKIGHWMCmhhc191aW50MzKUSwGMCHVpbnRlZ2VylErze2gMdWJ1Yi4=", "n": "3", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "n_envs": 1, "buffer_size": 50000, "batch_size": 32, "learning_starts": 50000, "tau": 1.0, "gamma": 0.99, "gradient_steps": 1, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==", "__module__": "stable_baselines3.common.buffers", "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ", "__init__": "<function ReplayBuffer.__init__ at 0x7effd56f6ef0>", "add": "<function ReplayBuffer.add at 0x7effd56f6f80>", "sample": "<function ReplayBuffer.sample at 0x7effd56f7010>", "_get_samples": "<function ReplayBuffer._get_samples at 0x7effd56f70a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7effd56fda40>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLBGgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "exploration_initial_eps": 1.0, "exploration_final_eps": 0.1, "exploration_fraction": 0.1, "target_update_interval": 10000, "_n_calls": 100000, "max_grad_norm": 10, "exploration_rate": 0.1, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "batch_norm_stats": [], "batch_norm_stats_target": [], "exploration_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLmRxbi5wb2xpY2llc5SMCURRTlBvbGljeZSTlC4=", "__module__": "stable_baselines3.dqn.policies", "__annotations__": "{'q_net': <class 'stable_baselines3.dqn.policies.QNetwork'>, 'q_net_target': <class 'stable_baselines3.dqn.policies.QNetwork'>}", "__doc__": "\n    Policy class with Q-Value Net and target net for DQN\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function DQNPolicy.__init__ at 0x7effd5716a70>", "_build": "<function DQNPolicy._build at 0x7effd5716b00>", "make_q_net": "<function DQNPolicy.make_q_net at 0x7effd5716b90>", "forward": "<function DQNPolicy.forward at 0x7effd5716c20>", "_predict": "<function DQNPolicy._predict at 0x7effd5716cb0>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7effd5716d40>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7effd5716dd0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7effd571a880>"}, "verbose": 0, "policy_kwargs": {}, "num_timesteps": 500000, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1697392684824796108, "learning_rate": 0.001, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAI9Vlb7YXiE8lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwKGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAAGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAIZgmr4hTSo8lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwKGlIwBQ5R0lFKULg=="}, "_episode_num": 2665, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 112500, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True]", "bounded_above": "[ True  True]", "_shape": [2], "low": "[-1.2  -0.07]", "high": "[0.6  0.07]", "low_repr": "[-1.2  -0.07]", "high_repr": "[0.6  0.07]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "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", "n": "3", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "n_envs": 1, "buffer_size": 50000, "batch_size": 32, "learning_starts": 50000, "tau": 1.0, "gamma": 0.99, "gradient_steps": 1, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==", "__module__": "stable_baselines3.common.buffers", "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ", "__init__": "<function ReplayBuffer.__init__ at 0x7effd56f6ef0>", "add": "<function ReplayBuffer.add at 0x7effd56f6f80>", "sample": "<function ReplayBuffer.sample at 0x7effd56f7010>", "_get_samples": "<function ReplayBuffer._get_samples at 0x7effd56f70a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7effd56fda40>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLBGgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "exploration_initial_eps": 1.0, "exploration_final_eps": 0.1, "exploration_fraction": 0.1, "target_update_interval": 10000, "_n_calls": 500000, "max_grad_norm": 10, "exploration_rate": 0.1, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "batch_norm_stats": [], "batch_norm_stats_target": [], "exploration_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

dqn-MountainCar-v0.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3d76b8d49f51025de1a192bf0805c3ae927ea0cb555acd606018a62f8a60ee6b
-size 97329
+oid sha256:a516856227a210ad918e7f443f150ce6769484c4204ffe12c176ecd2733a3983
+size 97335

dqn-MountainCar-v0/data

@@ -17,17 +17,17 @@
     },
     "verbose": 0,
     "policy_kwargs": {},
-    "num_timesteps": 100000,
-    "_total_timesteps": 100000,
+    "num_timesteps": 500000,
+    "_total_timesteps": 500000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1697392373848211472,
+    "start_time": 1697392684824796108,
     "learning_rate": 0.001,
     "tensorboard_log": null,
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAPiHF78AAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwKGlIwBQ5R0lFKULg=="
+        ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAI9Vlb7YXiE8lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwKGlIwBQ5R0lFKULg=="
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -35,22 +35,22 @@
     },
     "_last_original_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAANjrTb8j6eQ7lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwKGlIwBQ5R0lFKULg=="
+        ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAIZgmr4hTSo8lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwKGlIwBQ5R0lFKULg=="
     },
-    "_episode_num": 500,
+    "_episode_num": 2665,
     "use_sde": false,
     "sde_sample_freq": -1,
     "_current_progress_remaining": 0.0,
     "_stats_window_size": 100,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 12500,
+    "_n_updates": 112500,
     "observation_space": {
         ":type:": "<class 'gymnasium.spaces.box.Box'>",
         ":serialized:": "gAWVngEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAgAAAAAAAAABAZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksChZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoECiWAgAAAAAAAAABAZRoFEsChZRoGHSUUpSMBl9zaGFwZZRLAoWUjANsb3eUaBAolggAAAAAAAAAmpmZvylcj72UaApLAoWUaBh0lFKUjARoaWdolGgQKJYIAAAAAAAAAJqZGT8pXI89lGgKSwKFlGgYdJRSlIwIbG93X3JlcHKUjA1bLTEuMiAgLTAuMDddlIwJaGlnaF9yZXBylIwLWzAuNiAgMC4wN12UjApfbnBfcmFuZG9tlE51Yi4=",
@@ -68,7 +68,7 @@
     },
     "action_space": {
         ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
-        ":serialized:": "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",
+        ":serialized:": "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",
         "n": "3",
         "start": "0",
         "_shape": [],
@@ -105,7 +105,7 @@
     "exploration_final_eps": 0.1,
     "exploration_fraction": 0.1,
     "target_update_interval": 10000,
-    "_n_calls": 100000,
+    "_n_calls": 500000,
     "max_grad_norm": 10,
     "exploration_rate": 0.1,
     "lr_schedule": {

dqn-MountainCar-v0/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5cd45a47fe3c6122c6250b1943d07ec92a7994127c68e8816814ff20d82d2276
+oid sha256:dbe7822813d410597e3d0205f7a659e993e9ca020e7f223d1d5b0a5be2af7374
 size 41327

dqn-MountainCar-v0/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:92ecd4fee584b7386d978f117633700a5df777214affe6d1d1a6e38ddb482558
+oid sha256:195acdba12d4ad659e4691071ecbd90283f6ad3137e23db21ddf8ff30592abae
 size 40449

results.json

@@ -1 +1 @@
-{"mean_reward": -200.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-15T17:56:04.397660"}
+{"mean_reward": -160.6, "std_reward": 39.20765231431232, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-15T18:08:32.356120"}