Hugging Face's logo

apetrenko
/
sample_factory_brax_walker2d

Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
Eval Results
Model card Files Metrics Community
sample_factory_brax_walker2d
File size: 24,352 Bytes 
f25e361
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
 
[2023-01-11 19:27:33,824][2153185] Saving configuration to ./train_dir/v083_brax_basic_benchmark/v083_brax_basic_benchmark_slurm/06_v083_brax_basic_benchmark_see_2322090_env_walker2d_u.rnn_False_n.epo_5/config.json...
[2023-01-11 19:27:34,003][2153185] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-01-11 19:27:34,005][2153185] Rollout worker 0 uses device cuda:0
[2023-01-11 19:27:34,006][2153185] In synchronous mode, we only accumulate one batch. Setting num_batches_to_accumulate to 1
[2023-01-11 19:27:34,067][2153185] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-01-11 19:27:34,068][2153185] InferenceWorker_p0-w0: min num requests: 1
[2023-01-11 19:27:34,069][2153185] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-01-11 19:27:34,070][2153185] WARNING! It is generally recommended to enable Fixed KL loss (https://arxiv.org/pdf/1707.06347.pdf) for continuous action tasks to avoid potential numerical issues. I.e. set --kl_loss_coeff=0.1
[2023-01-11 19:27:34,070][2153185] Setting fixed seed 2322090
[2023-01-11 19:27:34,071][2153185] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-01-11 19:27:34,071][2153185] Initializing actor-critic model on device cuda:0
[2023-01-11 19:27:34,072][2153185] RunningMeanStd input shape: (17,)
[2023-01-11 19:27:34,072][2153185] RunningMeanStd input shape: (1,)
[2023-01-11 19:27:34,154][2153185] Created Actor Critic model with architecture:
[2023-01-11 19:27:34,154][2153185] ActorCriticSharedWeights(
  (obs_normalizer): ObservationNormalizer(
    (running_mean_std): RunningMeanStdDictInPlace(
      (running_mean_std): ModuleDict(
        (obs): RunningMeanStdInPlace()
      )
    )
  )
  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
  (encoder): MultiInputEncoder(
    (encoders): ModuleDict(
      (obs): MlpEncoder(
        (mlp_head): RecursiveScriptModule(
          original_name=Sequential
          (0): RecursiveScriptModule(original_name=Linear)
          (1): RecursiveScriptModule(original_name=ELU)
          (2): RecursiveScriptModule(original_name=Linear)
          (3): RecursiveScriptModule(original_name=ELU)
          (4): RecursiveScriptModule(original_name=Linear)
          (5): RecursiveScriptModule(original_name=ELU)
        )
      )
    )
  )
  (core): ModelCoreIdentity()
  (decoder): MlpDecoder(
    (mlp): Identity()
  )
  (critic_linear): Linear(in_features=64, out_features=1, bias=True)
  (action_parameterization): ActionParameterizationContinuousNonAdaptiveStddev(
    (distribution_linear): Linear(in_features=64, out_features=6, bias=True)
  )
)
[2023-01-11 19:27:34,156][2153185] Using optimizer <class 'torch.optim.adam.Adam'>
[2023-01-11 19:27:34,159][2153185] No checkpoints found
[2023-01-11 19:27:34,160][2153185] Did not load from checkpoint, starting from scratch!
[2023-01-11 19:27:34,161][2153185] Initialized policy 0 weights for model version 0
[2023-01-11 19:27:34,161][2153185] LearnerWorker_p0 finished initialization!
[2023-01-11 19:27:34,162][2153185] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-01-11 19:27:34,167][2153185] Inference worker 0-0 is ready!
[2023-01-11 19:27:34,167][2153185] All inference workers are ready! Signal rollout workers to start!
[2023-01-11 19:27:34,168][2153185] EnvRunner 0-0 uses policy 0
[2023-01-11 19:27:35,507][2153185] Resetting env <VectorGymWrapper instance> with 2048 parallel agents...
[2023-01-11 19:27:38,375][2153185] reset() done, obs.shape=torch.Size([2048, 17])!
[2023-01-11 19:27:47,676][2153185] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 0. Throughput: 0: nan. Samples: 2048. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
[2023-01-11 19:27:56,420][2153185] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 0.0. Samples: 2048. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
[2023-01-11 19:27:56,424][2153185] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 234.1. Samples: 4096. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
[2023-01-11 19:27:56,429][2153185] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 468.0. Samples: 6144. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
[2023-01-11 19:27:56,432][2153185] Heartbeat connected on Batcher_0
[2023-01-11 19:27:56,432][2153185] Heartbeat connected on LearnerWorker_p0
[2023-01-11 19:27:56,432][2153185] Heartbeat connected on InferenceWorker_p0-w0
[2023-01-11 19:27:56,432][2153185] Heartbeat connected on RolloutWorker_w0
[2023-01-11 19:27:58,473][2153185] Fps is (10 sec: 127937.9, 60 sec: 24280.7, 300 sec: 24280.7). Total num frames: 262144. Throughput: 0: 15365.1. Samples: 167936. Policy #0 lag: (min: 2.0, avg: 2.0, max: 2.0)
[2023-01-11 19:27:58,474][2153185] Avg episode reward: [(0, '23.990')]
[2023-01-11 19:28:03,423][2153185] Fps is (10 sec: 318590.1, 60 sec: 141502.0, 300 sec: 141502.0). Total num frames: 2228224. Throughput: 0: 87268.2. Samples: 1376256. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:28:03,424][2153185] Avg episode reward: [(0, '618.064')]
[2023-01-11 19:28:08,424][2153185] Fps is (10 sec: 381986.4, 60 sec: 195844.4, 300 sec: 195844.4). Total num frames: 4063232. Throughput: 0: 174917.4. Samples: 3631104. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:28:08,425][2153185] Avg episode reward: [(0, '630.121')]
[2023-01-11 19:28:13,422][2153185] Fps is (10 sec: 367046.5, 60 sec: 229095.9, 300 sec: 229095.9). Total num frames: 5898240. Throughput: 0: 227027.7. Samples: 5847040. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:28:13,423][2153185] Avg episode reward: [(0, '783.157')]
[2023-01-11 19:28:13,483][2153185] Saving new best policy, reward=783.157!
[2023-01-11 19:28:18,424][2153185] Fps is (10 sec: 373558.6, 60 sec: 253642.4, 300 sec: 253642.4). Total num frames: 7798784. Throughput: 0: 227065.9. Samples: 6983680. Policy #0 lag: (min: 1.0, avg: 1.0, max: 1.0)
[2023-01-11 19:28:18,424][2153185] Avg episode reward: [(0, '2312.752')]
[2023-01-11 19:28:18,432][2153185] Saving new best policy, reward=2312.752!
[2023-01-11 19:28:23,431][2153185] Fps is (10 sec: 379766.5, 60 sec: 271273.9, 300 sec: 271273.9). Total num frames: 9699328. Throughput: 0: 258386.1. Samples: 9240576. Policy #0 lag: (min: 0.0, avg: 0.0, max: 0.0)
[2023-01-11 19:28:23,432][2153185] Avg episode reward: [(0, '2002.332')]
[2023-01-11 19:28:28,423][2153185] Fps is (10 sec: 373562.0, 60 sec: 283072.2, 300 sec: 283072.2). Total num frames: 11534336. Throughput: 0: 358297.9. Samples: 11468800. Policy #0 lag: (min: 5.0, avg: 5.0, max: 5.0)
[2023-01-11 19:28:28,424][2153185] Avg episode reward: [(0, '2368.916')]
[2023-01-11 19:28:28,429][2153185] Saving new best policy, reward=2368.916!
[2023-01-11 19:28:33,443][2153185] Fps is (10 sec: 373110.6, 60 sec: 293551.7, 300 sec: 293551.7). Total num frames: 13434880. Throughput: 0: 339901.9. Samples: 12587008. Policy #0 lag: (min: 5.0, avg: 5.0, max: 5.0)
[2023-01-11 19:28:33,444][2153185] Avg episode reward: [(0, '2996.578')]
[2023-01-11 19:28:33,446][2153185] Saving new best policy, reward=2996.578!
[2023-01-11 19:28:38,422][2153185] Fps is (10 sec: 373599.0, 60 sec: 300909.4, 300 sec: 300909.4). Total num frames: 15269888. Throughput: 0: 352559.6. Samples: 14811136. Policy #0 lag: (min: 7.0, avg: 7.0, max: 7.0)
[2023-01-11 19:28:38,423][2153185] Avg episode reward: [(0, '3076.930')]
[2023-01-11 19:28:38,431][2153185] Saving new best policy, reward=3076.930!
[2023-01-11 19:28:43,424][2153185] Fps is (10 sec: 367721.8, 60 sec: 363906.9, 300 sec: 306830.4). Total num frames: 17104896. Throughput: 0: 375696.5. Samples: 17055744. Policy #0 lag: (min: 7.0, avg: 7.0, max: 7.0)
[2023-01-11 19:28:43,424][2153185] Avg episode reward: [(0, '3859.353')]
[2023-01-11 19:28:43,428][2153185] Saving new best policy, reward=3859.353!
[2023-01-11 19:28:48,422][2153185] Fps is (10 sec: 373566.4, 60 sec: 365502.6, 300 sec: 312869.9). Total num frames: 19005440. Throughput: 0: 372839.0. Samples: 18153472. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:28:48,423][2153185] Avg episode reward: [(0, '3764.592')]
[2023-01-11 19:28:53,424][2153185] Fps is (10 sec: 373543.7, 60 sec: 365657.5, 300 sec: 316977.7). Total num frames: 20840448. Throughput: 0: 373417.6. Samples: 20434944. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:28:53,425][2153185] Avg episode reward: [(0, '4154.622')]
[2023-01-11 19:28:53,429][2153185] Saving new best policy, reward=4154.622!
[2023-01-11 19:28:58,456][2153185] Fps is (10 sec: 372296.1, 60 sec: 374754.2, 300 sec: 321294.3). Total num frames: 22740992. Throughput: 0: 373094.6. Samples: 22648832. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:28:58,457][2153185] Avg episode reward: [(0, '4481.427')]
[2023-01-11 19:28:58,463][2153185] Saving new best policy, reward=4481.427!
[2023-01-11 19:29:03,470][2153185] Fps is (10 sec: 384889.5, 60 sec: 374357.4, 300 sec: 325979.1). Total num frames: 24707072. Throughput: 0: 373080.5. Samples: 23789568. Policy #0 lag: (min: 4.0, avg: 4.0, max: 4.0)
[2023-01-11 19:29:03,471][2153185] Avg episode reward: [(0, '4315.631')]
[2023-01-11 19:29:08,424][2153185] Fps is (10 sec: 381321.0, 60 sec: 374645.7, 300 sec: 328704.5). Total num frames: 26542080. Throughput: 0: 375071.6. Samples: 26116096. Policy #0 lag: (min: 4.0, avg: 4.0, max: 4.0)
[2023-01-11 19:29:08,425][2153185] Avg episode reward: [(0, '5075.545')]
[2023-01-11 19:29:08,433][2153185] Saving new best policy, reward=5075.545!
[2023-01-11 19:29:12,089][2153185] Early stopping after 3 epochs (6 sgd steps), loss delta 0.0000006
[2023-01-11 19:29:13,468][2153185] Fps is (10 sec: 380166.0, 60 sec: 376542.0, 300 sec: 332294.2). Total num frames: 28508160. Throughput: 0: 375819.4. Samples: 28397568. Policy #0 lag: (min: 7.0, avg: 7.0, max: 7.0)
[2023-01-11 19:29:13,469][2153185] Avg episode reward: [(0, '4851.746')]
[2023-01-11 19:29:17,083][2153185] Early stopping after 5 epochs (10 sgd steps), loss delta 0.0000005
[2023-01-11 19:29:18,424][2153185] Fps is (10 sec: 380103.1, 60 sec: 375736.5, 300 sec: 334368.5). Total num frames: 30343168. Throughput: 0: 377264.1. Samples: 29556736. Policy #0 lag: (min: 7.0, avg: 7.0, max: 7.0)
[2023-01-11 19:29:18,425][2153185] Avg episode reward: [(0, '3804.353')]
[2023-01-11 19:29:23,430][2153185] Fps is (10 sec: 374981.1, 60 sec: 375744.9, 300 sec: 336735.2). Total num frames: 32243712. Throughput: 0: 377128.2. Samples: 31784960. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:29:23,431][2153185] Avg episode reward: [(0, '4149.015')]
[2023-01-11 19:29:28,424][2153185] Fps is (10 sec: 373559.1, 60 sec: 375736.0, 300 sec: 338258.5). Total num frames: 34078720. Throughput: 0: 376827.7. Samples: 34013184. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:29:28,425][2153185] Avg episode reward: [(0, '4615.276')]
[2023-01-11 19:29:28,433][2153185] Saving ./train_dir/v083_brax_basic_benchmark/v083_brax_basic_benchmark_slurm/06_v083_brax_basic_benchmark_see_2322090_env_walker2d_u.rnn_False_n.epo_5/checkpoint_p0/checkpoint_000005196_34078720.pth...
[2023-01-11 19:29:33,423][2153185] Fps is (10 sec: 373832.6, 60 sec: 375866.1, 300 sec: 340240.8). Total num frames: 35979264. Throughput: 0: 377460.9. Samples: 35139584. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:29:33,424][2153185] Avg episode reward: [(0, '5411.330')]
[2023-01-11 19:29:33,427][2153185] Saving new best policy, reward=5411.330!
[2023-01-11 19:29:38,480][2153185] Fps is (10 sec: 378011.1, 60 sec: 376472.7, 300 sec: 341866.0). Total num frames: 37879808. Throughput: 0: 376411.6. Samples: 37394432. Policy #0 lag: (min: 6.0, avg: 6.0, max: 6.0)
[2023-01-11 19:29:38,480][2153185] Avg episode reward: [(0, '5203.336')]
[2023-01-11 19:29:43,422][2153185] Fps is (10 sec: 373588.1, 60 sec: 376841.2, 300 sec: 343121.6). Total num frames: 39714816. Throughput: 0: 378071.0. Samples: 39649280. Policy #0 lag: (min: 6.0, avg: 6.0, max: 6.0)
[2023-01-11 19:29:43,423][2153185] Avg episode reward: [(0, '5195.713')]
[2023-01-11 19:29:48,444][2153185] Fps is (10 sec: 374897.6, 60 sec: 376695.4, 300 sec: 344591.4). Total num frames: 41615360. Throughput: 0: 377415.6. Samples: 40763392. Policy #0 lag: (min: 3.0, avg: 3.0, max: 3.0)
[2023-01-11 19:29:48,444][2153185] Avg episode reward: [(0, '4986.651')]
[2023-01-11 19:29:53,424][2153185] Fps is (10 sec: 373487.3, 60 sec: 376831.7, 300 sec: 345536.9). Total num frames: 43450368. Throughput: 0: 375558.6. Samples: 43016192. Policy #0 lag: (min: 3.0, avg: 3.0, max: 3.0)
[2023-01-11 19:29:53,425][2153185] Avg episode reward: [(0, '5789.914')]
[2023-01-11 19:29:53,428][2153185] Saving new best policy, reward=5789.914!
[2023-01-11 19:29:58,424][2153185] Fps is (10 sec: 374311.5, 60 sec: 377034.7, 300 sec: 346859.9). Total num frames: 45350912. Throughput: 0: 375659.2. Samples: 45285376. Policy #0 lag: (min: 3.0, avg: 3.0, max: 3.0)
[2023-01-11 19:29:58,424][2153185] Avg episode reward: [(0, '5175.173')]
[2023-01-11 19:30:03,424][2153185] Fps is (10 sec: 373562.6, 60 sec: 374936.3, 300 sec: 347601.4). Total num frames: 47185920. Throughput: 0: 374287.2. Samples: 46399488. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:03,424][2153185] Avg episode reward: [(0, '5716.311')]
[2023-01-11 19:30:08,481][2153185] Fps is (10 sec: 377936.6, 60 sec: 376474.3, 300 sec: 349079.7). Total num frames: 49152000. Throughput: 0: 374544.6. Samples: 48658432. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:08,482][2153185] Avg episode reward: [(0, '5753.585')]
[2023-01-11 19:30:13,423][2153185] Fps is (10 sec: 380151.6, 60 sec: 374933.9, 300 sec: 349834.5). Total num frames: 50987008. Throughput: 0: 375843.1. Samples: 50925568. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:13,423][2153185] Avg episode reward: [(0, '5924.315')]
[2023-01-11 19:30:13,427][2153185] Saving new best policy, reward=5924.315!
[2023-01-11 19:30:18,422][2153185] Fps is (10 sec: 375757.5, 60 sec: 375750.7, 300 sec: 350839.0). Total num frames: 52887552. Throughput: 0: 375698.9. Samples: 52045824. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:18,423][2153185] Avg episode reward: [(0, '6123.117')]
[2023-01-11 19:30:18,430][2153185] Saving new best policy, reward=6123.117!
[2023-01-11 19:30:23,424][2153185] Fps is (10 sec: 373514.6, 60 sec: 374689.5, 300 sec: 351355.1). Total num frames: 54722560. Throughput: 0: 375341.2. Samples: 54263808. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:23,424][2153185] Avg episode reward: [(0, '5322.819')]
[2023-01-11 19:30:28,423][2153185] Fps is (10 sec: 373533.7, 60 sec: 375746.5, 300 sec: 352250.9). Total num frames: 56623104. Throughput: 0: 375732.2. Samples: 56557568. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:28,423][2153185] Avg episode reward: [(0, '5822.090')]
[2023-01-11 19:30:33,424][2153185] Fps is (10 sec: 380091.8, 60 sec: 375732.6, 300 sec: 353088.9). Total num frames: 58523648. Throughput: 0: 375903.8. Samples: 57671680. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:33,425][2153185] Avg episode reward: [(0, '5254.855')]
[2023-01-11 19:30:38,462][2153185] Fps is (10 sec: 378651.3, 60 sec: 375853.0, 300 sec: 353802.7). Total num frames: 60424192. Throughput: 0: 376290.2. Samples: 59963392. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:38,462][2153185] Avg episode reward: [(0, '5823.812')]
[2023-01-11 19:30:43,424][2153185] Fps is (10 sec: 373560.6, 60 sec: 375728.0, 300 sec: 354253.7). Total num frames: 62259200. Throughput: 0: 375736.2. Samples: 62193664. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:43,425][2153185] Avg episode reward: [(0, '5819.084')]
[2023-01-11 19:30:48,476][2153185] Fps is (10 sec: 373017.6, 60 sec: 375538.5, 300 sec: 354867.0). Total num frames: 64159744. Throughput: 0: 375304.5. Samples: 63307776. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:48,477][2153185] Avg episode reward: [(0, '4931.663')]
[2023-01-11 19:30:53,422][2153185] Fps is (10 sec: 380175.9, 60 sec: 376842.7, 300 sec: 355649.0). Total num frames: 66060288. Throughput: 0: 376687.2. Samples: 65587200. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:53,423][2153185] Avg episode reward: [(0, '5748.724')]
[2023-01-11 19:30:58,423][2153185] Fps is (10 sec: 395291.0, 60 sec: 379017.5, 300 sec: 356975.1). Total num frames: 68091904. Throughput: 0: 379737.8. Samples: 68014080. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:30:58,424][2153185] Avg episode reward: [(0, '5415.865')]
[2023-01-11 19:31:03,441][2153185] Fps is (10 sec: 392479.6, 60 sec: 379999.5, 300 sec: 357533.9). Total num frames: 69992448. Throughput: 0: 379724.7. Samples: 69140480. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:31:03,442][2153185] Avg episode reward: [(0, '5741.709')]
[2023-01-11 19:31:08,422][2153185] Fps is (10 sec: 373597.7, 60 sec: 378294.7, 300 sec: 357803.1). Total num frames: 71827456. Throughput: 0: 380484.8. Samples: 71385088. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:31:08,423][2153185] Avg episode reward: [(0, '5806.858')]
[2023-01-11 19:31:13,425][2153185] Fps is (10 sec: 374146.0, 60 sec: 379000.1, 300 sec: 358340.1). Total num frames: 73728000. Throughput: 0: 379498.7. Samples: 73635840. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:31:13,426][2153185] Avg episode reward: [(0, '5602.576')]
[2023-01-11 19:31:18,435][2153185] Fps is (10 sec: 379630.6, 60 sec: 378937.9, 300 sec: 358840.0). Total num frames: 75628544. Throughput: 0: 380018.1. Samples: 74776576. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:31:18,436][2153185] Avg episode reward: [(0, '5567.227')]
[2023-01-11 19:31:23,423][2153185] Fps is (10 sec: 373655.4, 60 sec: 379023.9, 300 sec: 359049.8). Total num frames: 77463552. Throughput: 0: 379163.0. Samples: 77010944. Policy #0 lag: (min: 1.0, avg: 1.0, max: 1.0)
[2023-01-11 19:31:23,423][2153185] Avg episode reward: [(0, '6320.190')]
[2023-01-11 19:31:23,427][2153185] Saving new best policy, reward=6320.190!
[2023-01-11 19:31:28,423][2153185] Fps is (10 sec: 367428.0, 60 sec: 377922.6, 300 sec: 359228.6). Total num frames: 79298560. Throughput: 0: 378658.5. Samples: 79233024. Policy #0 lag: (min: 1.0, avg: 1.0, max: 1.0)
[2023-01-11 19:31:28,424][2153185] Avg episode reward: [(0, '6510.601')]
[2023-01-11 19:31:28,431][2153185] Saving ./train_dir/v083_brax_basic_benchmark/v083_brax_basic_benchmark_slurm/06_v083_brax_basic_benchmark_see_2322090_env_walker2d_u.rnn_False_n.epo_5/checkpoint_p0/checkpoint_000012096_79298560.pth...
[2023-01-11 19:31:28,499][2153185] Saving new best policy, reward=6510.601!
[2023-01-11 19:31:33,423][2153185] Fps is (10 sec: 366976.3, 60 sec: 376837.8, 300 sec: 359400.9). Total num frames: 81133568. Throughput: 0: 378048.5. Samples: 80300032. Policy #0 lag: (min: 1.0, avg: 1.0, max: 1.0)
[2023-01-11 19:31:33,424][2153185] Avg episode reward: [(0, '6344.935')]
[2023-01-11 19:31:38,466][2153185] Fps is (10 sec: 378491.2, 60 sec: 377895.9, 300 sec: 360066.8). Total num frames: 83099648. Throughput: 0: 377647.8. Samples: 82597888. Policy #0 lag: (min: 1.0, avg: 1.0, max: 1.0)
[2023-01-11 19:31:38,467][2153185] Avg episode reward: [(0, '6454.606')]
[2023-01-11 19:31:43,421][2153185] Fps is (10 sec: 380194.7, 60 sec: 377943.4, 300 sec: 360282.8). Total num frames: 84934656. Throughput: 0: 374349.5. Samples: 84858880. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:31:43,421][2153185] Avg episode reward: [(0, '6075.015')]
[2023-01-11 19:31:48,424][2153185] Fps is (10 sec: 375147.6, 60 sec: 378254.3, 300 sec: 360690.6). Total num frames: 86835200. Throughput: 0: 374792.6. Samples: 85999616. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:31:48,424][2153185] Avg episode reward: [(0, '6092.570')]
[2023-01-11 19:31:53,423][2153185] Fps is (10 sec: 380028.3, 60 sec: 377919.0, 300 sec: 361086.4). Total num frames: 88735744. Throughput: 0: 375369.1. Samples: 88276992. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:31:53,424][2153185] Avg episode reward: [(0, '5996.674')]
[2023-01-11 19:31:58,423][2153185] Fps is (10 sec: 373559.8, 60 sec: 374647.3, 300 sec: 361203.8). Total num frames: 90570752. Throughput: 0: 375071.7. Samples: 90513408. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:31:58,424][2153185] Avg episode reward: [(0, '6051.410')]
[2023-01-11 19:32:03,422][2153185] Fps is (10 sec: 373595.6, 60 sec: 374766.2, 300 sec: 361575.5). Total num frames: 92471296. Throughput: 0: 375119.1. Samples: 91652096. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:32:03,423][2153185] Avg episode reward: [(0, '6075.545')]
[2023-01-11 19:32:08,424][2153185] Fps is (10 sec: 380091.6, 60 sec: 375729.7, 300 sec: 361928.2). Total num frames: 94371840. Throughput: 0: 375819.3. Samples: 93923328. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:32:08,425][2153185] Avg episode reward: [(0, '5770.103')]
[2023-01-11 19:32:13,424][2153185] Fps is (10 sec: 373479.6, 60 sec: 374654.9, 300 sec: 362023.5). Total num frames: 96206848. Throughput: 0: 376097.7. Samples: 96157696. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:32:13,425][2153185] Avg episode reward: [(0, '6145.203')]
[2023-01-11 19:32:18,423][2153185] Fps is (10 sec: 386687.5, 60 sec: 376905.1, 300 sec: 362842.7). Total num frames: 98238464. Throughput: 0: 379653.4. Samples: 97384448. Policy #0 lag: (min: 9.0, avg: 9.0, max: 9.0)
[2023-01-11 19:32:18,424][2153185] Avg episode reward: [(0, '6026.312')]
[2023-01-11 19:32:23,203][2153185] Saving ./train_dir/v083_brax_basic_benchmark/v083_brax_basic_benchmark_slurm/06_v083_brax_basic_benchmark_see_2322090_env_walker2d_u.rnn_False_n.epo_5/checkpoint_p0/checkpoint_000015266_100073472.pth...
[2023-01-11 19:32:23,219][2153185] Removing ./train_dir/v083_brax_basic_benchmark/v083_brax_basic_benchmark_slurm/06_v083_brax_basic_benchmark_see_2322090_env_walker2d_u.rnn_False_n.epo_5/checkpoint_p0/checkpoint_000005196_34078720.pth
[2023-01-11 19:32:23,220][2153185] Stopping Batcher_0...
[2023-01-11 19:32:23,221][2153185] Stopping InferenceWorker_p0-w0...
[2023-01-11 19:32:23,221][2153185] Stopping RolloutWorker_w0...
[2023-01-11 19:32:23,221][2153185] Saving ./train_dir/v083_brax_basic_benchmark/v083_brax_basic_benchmark_slurm/06_v083_brax_basic_benchmark_see_2322090_env_walker2d_u.rnn_False_n.epo_5/checkpoint_p0/checkpoint_000015266_100073472.pth...
[2023-01-11 19:32:23,235][2153185] Stopping LearnerWorker_p0...
[2023-01-11 19:32:23,235][2153185] Component Batcher_0 stopped!
[2023-01-11 19:32:23,236][2153185] Component InferenceWorker_p0-w0 stopped!
[2023-01-11 19:32:23,236][2153185] Component RolloutWorker_w0 stopped!
[2023-01-11 19:32:23,236][2153185] Component LearnerWorker_p0 stopped!
[2023-01-11 19:32:23,236][2153185] Batcher 0 profile tree view:
batching: 0.3521, releasing_batches: 0.0642
[2023-01-11 19:32:23,236][2153185] InferenceWorker_p0-w0 profile tree view:
update_model: 0.4320
one_step: 0.0012
  handle_policy_step: 57.3554
    deserialize: 0.4865, stack: 0.0681, obs_to_device_normalize: 10.3711, forward: 35.6708, prepare_outputs: 6.7457, send_messages: 0.8052
[2023-01-11 19:32:23,237][2153185] Learner 0 profile tree view:
misc: 0.0050, prepare_batch: 5.5690
train: 87.4892
  epoch_init: 0.0595, minibatch_init: 0.9957, losses_postprocess: 3.1702, kl_divergence: 5.7630, after_optimizer: 0.3136
  calculate_losses: 17.7913
    losses_init: 0.0331, forward_head: 2.8800, bptt_initial: 0.1263, bptt: 0.1281, tail: 8.7123, advantages_returns: 1.1141, losses: 3.4779
  update: 57.5417
    clip: 8.7673
[2023-01-11 19:32:23,237][2153185] RolloutWorker_w0 profile tree view:
wait_for_trajectories: 0.0819, enqueue_policy_requests: 5.6467, process_policy_outputs: 3.6701, env_step: 90.1515, finalize_trajectories: 0.1553, complete_rollouts: 0.0656
post_env_step: 19.1398
  process_env_step: 7.7900
[2023-01-11 19:32:23,237][2153185] Loop Runner_EvtLoop terminating...
[2023-01-11 19:32:23,237][2153185] Runner profile tree view:
main_loop: 289.1680
[2023-01-11 19:32:23,238][2153185] Collected {0: 100073472}, FPS: 346073.8