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[2023-08-17 11:34:50,384][121125] Saving configuration to /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/config.json...
[2023-08-17 11:34:50,404][121125] Rollout worker 0 uses device cpu
[2023-08-17 11:34:50,404][121125] Rollout worker 1 uses device cpu
[2023-08-17 11:34:50,405][121125] Rollout worker 2 uses device cpu
[2023-08-17 11:34:50,405][121125] Rollout worker 3 uses device cpu
[2023-08-17 11:34:50,406][121125] Rollout worker 4 uses device cpu
[2023-08-17 11:34:50,406][121125] Rollout worker 5 uses device cpu
[2023-08-17 11:34:50,406][121125] Rollout worker 6 uses device cpu
[2023-08-17 11:34:50,406][121125] Rollout worker 7 uses device cpu
[2023-08-17 11:34:50,440][121125] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 11:34:50,441][121125] InferenceWorker_p0-w0: min num requests: 2
[2023-08-17 11:34:50,458][121125] Starting all processes...
[2023-08-17 11:34:50,458][121125] Starting process learner_proc0
[2023-08-17 11:34:50,508][121125] Starting all processes...
[2023-08-17 11:34:50,512][121125] Starting process inference_proc0-0
[2023-08-17 11:34:50,512][121125] Starting process rollout_proc0
[2023-08-17 11:34:50,513][121125] Starting process rollout_proc1
[2023-08-17 11:34:50,514][121125] Starting process rollout_proc2
[2023-08-17 11:34:50,514][121125] Starting process rollout_proc3
[2023-08-17 11:34:50,514][121125] Starting process rollout_proc4
[2023-08-17 11:34:50,514][121125] Starting process rollout_proc5
[2023-08-17 11:34:50,514][121125] Starting process rollout_proc6
[2023-08-17 11:34:50,515][121125] Starting process rollout_proc7
[2023-08-17 11:34:51,414][121211] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 11:34:51,414][121211] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
[2023-08-17 11:34:51,424][121211] Num visible devices: 1
[2023-08-17 11:34:51,443][121211] Starting seed is not provided
[2023-08-17 11:34:51,444][121211] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 11:34:51,444][121211] Initializing actor-critic model on device cuda:0
[2023-08-17 11:34:51,444][121211] RunningMeanStd input shape: (3, 72, 128)
[2023-08-17 11:34:51,445][121211] RunningMeanStd input shape: (1,)
[2023-08-17 11:34:51,456][121211] ConvEncoder: input_channels=3
[2023-08-17 11:34:51,528][121211] Conv encoder output size: 512
[2023-08-17 11:34:51,528][121211] Policy head output size: 512
[2023-08-17 11:34:51,541][121211] Created Actor Critic model with architecture:
[2023-08-17 11:34:51,541][121211] ActorCriticSharedWeights(
(obs_normalizer): ObservationNormalizer(
(running_mean_std): RunningMeanStdDictInPlace(
(running_mean_std): ModuleDict(
(obs): RunningMeanStdInPlace()
)
)
)
(returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
(encoder): VizdoomEncoder(
(basic_encoder): ConvEncoder(
(enc): RecursiveScriptModule(
original_name=ConvEncoderImpl
(conv_head): RecursiveScriptModule(
original_name=Sequential
(0): RecursiveScriptModule(original_name=Conv2d)
(1): RecursiveScriptModule(original_name=ELU)
(2): RecursiveScriptModule(original_name=Conv2d)
(3): RecursiveScriptModule(original_name=ELU)
(4): RecursiveScriptModule(original_name=Conv2d)
(5): RecursiveScriptModule(original_name=ELU)
)
(mlp_layers): RecursiveScriptModule(
original_name=Sequential
(0): RecursiveScriptModule(original_name=Linear)
(1): RecursiveScriptModule(original_name=ELU)
)
)
)
)
(core): ModelCoreRNN(
(core): GRU(512, 512)
)
(decoder): MlpDecoder(
(mlp): Identity()
)
(critic_linear): Linear(in_features=512, out_features=1, bias=True)
(action_parameterization): ActionParameterizationDefault(
(distribution_linear): Linear(in_features=512, out_features=5, bias=True)
)
)
[2023-08-17 11:34:51,559][121232] Worker 6 uses CPU cores [18, 19, 20]
[2023-08-17 11:34:51,561][121228] Worker 3 uses CPU cores [9, 10, 11]
[2023-08-17 11:34:51,567][121230] Worker 5 uses CPU cores [15, 16, 17]
[2023-08-17 11:34:51,567][121226] Worker 0 uses CPU cores [0, 1, 2]
[2023-08-17 11:34:51,573][121224] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 11:34:51,573][121224] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
[2023-08-17 11:34:51,581][121224] Num visible devices: 1
[2023-08-17 11:34:51,590][121225] Worker 1 uses CPU cores [3, 4, 5]
[2023-08-17 11:34:51,593][121229] Worker 4 uses CPU cores [12, 13, 14]
[2023-08-17 11:34:51,613][121231] Worker 7 uses CPU cores [21, 22, 23]
[2023-08-17 11:34:51,628][121227] Worker 2 uses CPU cores [6, 7, 8]
[2023-08-17 11:34:53,156][121211] Using optimizer <class 'torch.optim.adam.Adam'>
[2023-08-17 11:34:53,157][121211] No checkpoints found
[2023-08-17 11:34:53,157][121211] Did not load from checkpoint, starting from scratch!
[2023-08-17 11:34:53,157][121211] Initialized policy 0 weights for model version 0
[2023-08-17 11:34:53,158][121211] LearnerWorker_p0 finished initialization!
[2023-08-17 11:34:53,158][121211] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 11:34:53,455][121125] 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)
[2023-08-17 11:34:53,703][121224] RunningMeanStd input shape: (3, 72, 128)
[2023-08-17 11:34:53,703][121224] RunningMeanStd input shape: (1,)
[2023-08-17 11:34:53,710][121224] ConvEncoder: input_channels=3
[2023-08-17 11:34:53,760][121224] Conv encoder output size: 512
[2023-08-17 11:34:53,760][121224] Policy head output size: 512
[2023-08-17 11:34:54,313][121125] Inference worker 0-0 is ready!
[2023-08-17 11:34:54,314][121125] All inference workers are ready! Signal rollout workers to start!
[2023-08-17 11:34:54,329][121229] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 11:34:54,329][121226] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 11:34:54,329][121231] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 11:34:54,329][121232] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 11:34:54,330][121227] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 11:34:54,330][121228] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 11:34:54,333][121225] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 11:34:54,333][121230] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 11:34:54,460][121227] VizDoom game.init() threw an exception ViZDoomUnexpectedExitException('Controlled ViZDoom instance exited unexpectedly.'). Terminate process...
[2023-08-17 11:34:54,461][121227] EvtLoop [rollout_proc2_evt_loop, process=rollout_proc2] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Sampler', signal_name='_inference_workers_initialized'), args=()
Traceback (most recent call last):
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-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 "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
slot_callable(*args)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/rollout_worker.py", line 150, in init
env_runner.init(self.timing)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 418, in init
self._reset()
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-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 "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 414, in reset
return self.env.reset(seed=seed, options=options)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 125, in reset
obs, info = self.env.reset(**kwargs)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 110, in reset
obs, info = self.env.reset(**kwargs)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 30, in reset
return self.env.reset(**kwargs)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 462, in reset
obs, info = self.env.reset(seed=seed, options=options)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/envs/env_wrappers.py", line 82, in reset
obs, info = self.env.reset(**kwargs)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 414, in reset
return self.env.reset(seed=seed, options=options)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 51, in reset
return self.env.reset(**kwargs)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/doom_gym.py", line 323, in reset
self._ensure_initialized()
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/doom_gym.py", line 274, in _ensure_initialized
self.initialize()
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/doom_gym.py", line 269, in initialize
self._game_init()
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/doom_gym.py", line 244, in _game_init
raise EnvCriticalError()
sample_factory.envs.env_utils.EnvCriticalError
[2023-08-17 11:34:54,462][121227] Unhandled exception in evt loop rollout_proc2_evt_loop
[2023-08-17 11:34:54,535][121231] Decorrelating experience for 0 frames...
[2023-08-17 11:34:54,544][121226] Decorrelating experience for 0 frames...
[2023-08-17 11:34:54,548][121232] Decorrelating experience for 0 frames...
[2023-08-17 11:34:54,550][121228] Decorrelating experience for 0 frames...
[2023-08-17 11:34:54,550][121230] Decorrelating experience for 0 frames...
[2023-08-17 11:34:54,728][121231] Decorrelating experience for 32 frames...
[2023-08-17 11:34:54,746][121229] Decorrelating experience for 0 frames...
[2023-08-17 11:34:54,747][121228] Decorrelating experience for 32 frames...
[2023-08-17 11:34:54,747][121230] Decorrelating experience for 32 frames...
[2023-08-17 11:34:54,748][121232] Decorrelating experience for 32 frames...
[2023-08-17 11:34:54,934][121229] Decorrelating experience for 32 frames...
[2023-08-17 11:34:54,934][121226] Decorrelating experience for 32 frames...
[2023-08-17 11:34:54,954][121230] Decorrelating experience for 64 frames...
[2023-08-17 11:34:54,954][121228] Decorrelating experience for 64 frames...
[2023-08-17 11:34:54,955][121232] Decorrelating experience for 64 frames...
[2023-08-17 11:34:55,137][121226] Decorrelating experience for 64 frames...
[2023-08-17 11:34:55,138][121229] Decorrelating experience for 64 frames...
[2023-08-17 11:34:55,138][121231] Decorrelating experience for 64 frames...
[2023-08-17 11:34:55,138][121225] Decorrelating experience for 0 frames...
[2023-08-17 11:34:55,144][121232] Decorrelating experience for 96 frames...
[2023-08-17 11:34:55,336][121225] Decorrelating experience for 32 frames...
[2023-08-17 11:34:55,338][121231] Decorrelating experience for 96 frames...
[2023-08-17 11:34:55,367][121228] Decorrelating experience for 96 frames...
[2023-08-17 11:34:55,519][121225] Decorrelating experience for 64 frames...
[2023-08-17 11:34:55,524][121226] Decorrelating experience for 96 frames...
[2023-08-17 11:34:55,734][121230] Decorrelating experience for 96 frames...
[2023-08-17 11:34:55,737][121229] Decorrelating experience for 96 frames...
[2023-08-17 11:34:55,742][121225] Decorrelating experience for 96 frames...
[2023-08-17 11:34:56,232][121211] Signal inference workers to stop experience collection...
[2023-08-17 11:34:56,234][121224] InferenceWorker_p0-w0: stopping experience collection
[2023-08-17 11:34:57,320][121211] Signal inference workers to resume experience collection...
[2023-08-17 11:34:57,320][121224] InferenceWorker_p0-w0: resuming experience collection
[2023-08-17 11:34:58,455][121125] Fps is (10 sec: 6553.7, 60 sec: 6553.7, 300 sec: 6553.7). Total num frames: 32768. Throughput: 0: 566.8. Samples: 2834. Policy #0 lag: (min: 0.0, avg: 0.0, max: 0.0)
[2023-08-17 11:34:58,456][121125] Avg episode reward: [(0, '3.897')]
[2023-08-17 11:34:58,583][121224] Updated weights for policy 0, policy_version 10 (0.0188)
[2023-08-17 11:34:59,640][121224] Updated weights for policy 0, policy_version 20 (0.0006)
[2023-08-17 11:35:00,607][121224] Updated weights for policy 0, policy_version 30 (0.0005)
[2023-08-17 11:35:01,567][121224] Updated weights for policy 0, policy_version 40 (0.0005)
[2023-08-17 11:35:02,584][121224] Updated weights for policy 0, policy_version 50 (0.0006)
[2023-08-17 11:35:03,455][121125] Fps is (10 sec: 23347.3, 60 sec: 23347.3, 300 sec: 23347.3). Total num frames: 233472. Throughput: 0: 5847.0. Samples: 58470. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-08-17 11:35:03,456][121125] Avg episode reward: [(0, '4.655')]
[2023-08-17 11:35:03,457][121211] Saving new best policy, reward=4.655!
[2023-08-17 11:35:03,725][121224] Updated weights for policy 0, policy_version 60 (0.0006)
[2023-08-17 11:35:04,740][121224] Updated weights for policy 0, policy_version 70 (0.0006)
[2023-08-17 11:35:05,714][121224] Updated weights for policy 0, policy_version 80 (0.0005)
[2023-08-17 11:35:06,667][121224] Updated weights for policy 0, policy_version 90 (0.0005)
[2023-08-17 11:35:07,660][121224] Updated weights for policy 0, policy_version 100 (0.0005)
[2023-08-17 11:35:08,455][121125] Fps is (10 sec: 40959.9, 60 sec: 29491.3, 300 sec: 29491.3). Total num frames: 442368. Throughput: 0: 5926.0. Samples: 88890. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-08-17 11:35:08,456][121125] Avg episode reward: [(0, '4.598')]
[2023-08-17 11:35:08,628][121224] Updated weights for policy 0, policy_version 110 (0.0005)
[2023-08-17 11:35:09,699][121224] Updated weights for policy 0, policy_version 120 (0.0006)
[2023-08-17 11:35:10,436][121125] Heartbeat connected on Batcher_0
[2023-08-17 11:35:10,438][121125] Heartbeat connected on LearnerWorker_p0
[2023-08-17 11:35:10,442][121125] Heartbeat connected on InferenceWorker_p0-w0
[2023-08-17 11:35:10,444][121125] Heartbeat connected on RolloutWorker_w0
[2023-08-17 11:35:10,446][121125] Heartbeat connected on RolloutWorker_w1
[2023-08-17 11:35:10,450][121125] Heartbeat connected on RolloutWorker_w3
[2023-08-17 11:35:10,453][121125] Heartbeat connected on RolloutWorker_w4
[2023-08-17 11:35:10,455][121125] Heartbeat connected on RolloutWorker_w5
[2023-08-17 11:35:10,456][121125] Heartbeat connected on RolloutWorker_w6
[2023-08-17 11:35:10,459][121125] Heartbeat connected on RolloutWorker_w7
[2023-08-17 11:35:10,742][121224] Updated weights for policy 0, policy_version 130 (0.0005)
[2023-08-17 11:35:11,789][121224] Updated weights for policy 0, policy_version 140 (0.0005)
[2023-08-17 11:35:12,785][121224] Updated weights for policy 0, policy_version 150 (0.0005)
[2023-08-17 11:35:13,455][121125] Fps is (10 sec: 40550.2, 60 sec: 31948.8, 300 sec: 31948.8). Total num frames: 638976. Throughput: 0: 7468.3. Samples: 149366. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-08-17 11:35:13,456][121125] Avg episode reward: [(0, '4.646')]
[2023-08-17 11:35:13,856][121224] Updated weights for policy 0, policy_version 160 (0.0007)
[2023-08-17 11:35:14,953][121224] Updated weights for policy 0, policy_version 170 (0.0007)
[2023-08-17 11:35:15,042][121230] EvtLoop [rollout_proc5_evt_loop, process=rollout_proc5] unhandled exception in slot='advance_rollouts' connected to emitter=Emitter(object_id='InferenceWorker_p0-w0', signal_name='advance5'), args=(0, 0)
Traceback (most recent call last):
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
slot_callable(*args)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/rollout_worker.py", line 241, in advance_rollouts
complete_rollouts, episodic_stats = runner.advance_rollouts(policy_id, self.timing)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 634, in advance_rollouts
new_obs, rewards, terminated, truncated, infos = e.step(actions)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 129, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 115, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 33, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 469, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/envs/env_wrappers.py", line 86, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 54, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/doom_gym.py", line 452, in step
reward = self.game.make_action(actions_flattened, self.skip_frames)
vizdoom.vizdoom.SignalException: Signal SIGINT received. ViZDoom instance has been closed.
[2023-08-17 11:35:15,042][121229] EvtLoop [rollout_proc4_evt_loop, process=rollout_proc4] unhandled exception in slot='advance_rollouts' connected to emitter=Emitter(object_id='InferenceWorker_p0-w0', signal_name='advance4'), args=(1, 0)
Traceback (most recent call last):
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
slot_callable(*args)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/rollout_worker.py", line 241, in advance_rollouts
complete_rollouts, episodic_stats = runner.advance_rollouts(policy_id, self.timing)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 634, in advance_rollouts
new_obs, rewards, terminated, truncated, infos = e.step(actions)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 129, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 115, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 33, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 469, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/envs/env_wrappers.py", line 86, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 54, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/doom_gym.py", line 452, in step
reward = self.game.make_action(actions_flattened, self.skip_frames)
vizdoom.vizdoom.SignalException: Signal SIGINT received. ViZDoom instance has been closed.
[2023-08-17 11:35:15,043][121230] Unhandled exception Signal SIGINT received. ViZDoom instance has been closed. in evt loop rollout_proc5_evt_loop
[2023-08-17 11:35:15,043][121229] Unhandled exception Signal SIGINT received. ViZDoom instance has been closed. in evt loop rollout_proc4_evt_loop
[2023-08-17 11:35:15,042][121228] EvtLoop [rollout_proc3_evt_loop, process=rollout_proc3] unhandled exception in slot='advance_rollouts' connected to emitter=Emitter(object_id='InferenceWorker_p0-w0', signal_name='advance3'), args=(0, 0)
Traceback (most recent call last):
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
slot_callable(*args)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/rollout_worker.py", line 241, in advance_rollouts
complete_rollouts, episodic_stats = runner.advance_rollouts(policy_id, self.timing)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 634, in advance_rollouts
new_obs, rewards, terminated, truncated, infos = e.step(actions)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 129, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 115, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 33, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 469, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/envs/env_wrappers.py", line 86, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 54, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/doom_gym.py", line 452, in step
reward = self.game.make_action(actions_flattened, self.skip_frames)
vizdoom.vizdoom.SignalException: Signal SIGINT received. ViZDoom instance has been closed.
[2023-08-17 11:35:15,044][121228] Unhandled exception Signal SIGINT received. ViZDoom instance has been closed. in evt loop rollout_proc3_evt_loop
[2023-08-17 11:35:15,043][121231] EvtLoop [rollout_proc7_evt_loop, process=rollout_proc7] unhandled exception in slot='advance_rollouts' connected to emitter=Emitter(object_id='InferenceWorker_p0-w0', signal_name='advance7'), args=(0, 0)
Traceback (most recent call last):
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
slot_callable(*args)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/rollout_worker.py", line 241, in advance_rollouts
complete_rollouts, episodic_stats = runner.advance_rollouts(policy_id, self.timing)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 634, in advance_rollouts
new_obs, rewards, terminated, truncated, infos = e.step(actions)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 129, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 115, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 33, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 469, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/envs/env_wrappers.py", line 86, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 54, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/doom_gym.py", line 452, in step
reward = self.game.make_action(actions_flattened, self.skip_frames)
vizdoom.vizdoom.SignalException: Signal SIGINT received. ViZDoom instance has been closed.
[2023-08-17 11:35:15,044][121231] Unhandled exception Signal SIGINT received. ViZDoom instance has been closed. in evt loop rollout_proc7_evt_loop
[2023-08-17 11:35:15,046][121232] EvtLoop [rollout_proc6_evt_loop, process=rollout_proc6] unhandled exception in slot='advance_rollouts' connected to emitter=Emitter(object_id='InferenceWorker_p0-w0', signal_name='advance6'), args=(0, 0)
Traceback (most recent call last):
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
slot_callable(*args)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/rollout_worker.py", line 241, in advance_rollouts
complete_rollouts, episodic_stats = runner.advance_rollouts(policy_id, self.timing)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 634, in advance_rollouts
new_obs, rewards, terminated, truncated, infos = e.step(actions)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 129, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 115, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 33, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 469, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/envs/env_wrappers.py", line 86, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 54, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/doom_gym.py", line 452, in step
reward = self.game.make_action(actions_flattened, self.skip_frames)
vizdoom.vizdoom.SignalException: Signal SIGINT received. ViZDoom instance has been closed.
[2023-08-17 11:35:15,047][121232] Unhandled exception Signal SIGINT received. ViZDoom instance has been closed. in evt loop rollout_proc6_evt_loop
[2023-08-17 11:35:15,046][121225] EvtLoop [rollout_proc1_evt_loop, process=rollout_proc1] unhandled exception in slot='advance_rollouts' connected to emitter=Emitter(object_id='InferenceWorker_p0-w0', signal_name='advance1'), args=(1, 0)
Traceback (most recent call last):
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
slot_callable(*args)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/rollout_worker.py", line 241, in advance_rollouts
complete_rollouts, episodic_stats = runner.advance_rollouts(policy_id, self.timing)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 634, in advance_rollouts
new_obs, rewards, terminated, truncated, infos = e.step(actions)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 129, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 115, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 33, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 469, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/envs/env_wrappers.py", line 86, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 54, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/doom_gym.py", line 452, in step
reward = self.game.make_action(actions_flattened, self.skip_frames)
vizdoom.vizdoom.SignalException: Signal SIGINT received. ViZDoom instance has been closed.
[2023-08-17 11:35:15,047][121225] Unhandled exception Signal SIGINT received. ViZDoom instance has been closed. in evt loop rollout_proc1_evt_loop
[2023-08-17 11:35:15,042][121226] EvtLoop [rollout_proc0_evt_loop, process=rollout_proc0] unhandled exception in slot='advance_rollouts' connected to emitter=Emitter(object_id='InferenceWorker_p0-w0', signal_name='advance0'), args=(0, 0)
Traceback (most recent call last):
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/signal_slot/signal_slot.py", line 355, in _process_signal
slot_callable(*args)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/rollout_worker.py", line 241, in advance_rollouts
complete_rollouts, episodic_stats = runner.advance_rollouts(policy_id, self.timing)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 634, in advance_rollouts
new_obs, rewards, terminated, truncated, infos = e.step(actions)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 129, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/algo/utils/make_env.py", line 115, in step
obs, rew, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 33, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 469, in step
observation, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sample_factory/envs/env_wrappers.py", line 86, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/nix/store/b84h28azn9cg3h9940zb3b3x2569sykl-python3-3.10.12-env/lib/python3.10/site-packages/gymnasium/core.py", line 408, in step
return self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 54, in step
obs, reward, terminated, truncated, info = self.env.step(action)
File "/home/patonw/code/learn/deep-rl-class/.mypy/lib/python3.10/site-packages/sf_examples/vizdoom/doom/doom_gym.py", line 452, in step
reward = self.game.make_action(actions_flattened, self.skip_frames)
vizdoom.vizdoom.SignalException: Signal SIGINT received. ViZDoom instance has been closed.
[2023-08-17 11:35:15,048][121226] Unhandled exception Signal SIGINT received. ViZDoom instance has been closed. in evt loop rollout_proc0_evt_loop
[2023-08-17 11:35:15,058][121125] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 121125], exiting...
[2023-08-17 11:35:15,059][121125] Runner profile tree view:
main_loop: 24.6015
[2023-08-17 11:35:15,060][121211] Stopping Batcher_0...
[2023-08-17 11:35:15,060][121211] Loop batcher_evt_loop terminating...
[2023-08-17 11:35:15,060][121125] Collected {0: 696320}, FPS: 28303.9
[2023-08-17 11:35:15,061][121211] Saving /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000170_696320.pth...
[2023-08-17 11:35:15,107][121211] Stopping LearnerWorker_p0...
[2023-08-17 11:35:15,108][121211] Loop learner_proc0_evt_loop terminating...
[2023-08-17 11:35:15,121][121224] Weights refcount: 2 0
[2023-08-17 11:35:15,123][121224] Stopping InferenceWorker_p0-w0...
[2023-08-17 11:35:15,123][121224] Loop inference_proc0-0_evt_loop terminating...
[2023-08-17 12:08:48,041][131794] Saving configuration to /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/config.json...
[2023-08-17 12:08:48,053][131794] Rollout worker 0 uses device cpu
[2023-08-17 12:08:48,054][131794] Rollout worker 1 uses device cpu
[2023-08-17 12:08:48,054][131794] Rollout worker 2 uses device cpu
[2023-08-17 12:08:48,055][131794] Rollout worker 3 uses device cpu
[2023-08-17 12:08:48,055][131794] Rollout worker 4 uses device cpu
[2023-08-17 12:08:48,055][131794] Rollout worker 5 uses device cpu
[2023-08-17 12:08:48,056][131794] Rollout worker 6 uses device cpu
[2023-08-17 12:08:48,056][131794] Rollout worker 7 uses device cpu
[2023-08-17 12:08:48,086][131794] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 12:08:48,087][131794] InferenceWorker_p0-w0: min num requests: 2
[2023-08-17 12:08:48,104][131794] Starting all processes...
[2023-08-17 12:08:48,105][131794] Starting process learner_proc0
[2023-08-17 12:08:48,154][131794] Starting all processes...
[2023-08-17 12:08:48,158][131794] Starting process inference_proc0-0
[2023-08-17 12:08:48,158][131794] Starting process rollout_proc0
[2023-08-17 12:08:48,158][131794] Starting process rollout_proc1
[2023-08-17 12:08:48,159][131794] Starting process rollout_proc2
[2023-08-17 12:08:48,159][131794] Starting process rollout_proc3
[2023-08-17 12:08:48,160][131794] Starting process rollout_proc4
[2023-08-17 12:08:48,161][131794] Starting process rollout_proc5
[2023-08-17 12:08:48,162][131794] Starting process rollout_proc6
[2023-08-17 12:08:48,162][131794] Starting process rollout_proc7
[2023-08-17 12:08:49,071][131864] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 12:08:49,071][131864] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
[2023-08-17 12:08:49,082][131864] Num visible devices: 1
[2023-08-17 12:08:49,101][131864] Starting seed is not provided
[2023-08-17 12:08:49,102][131864] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 12:08:49,102][131864] Initializing actor-critic model on device cuda:0
[2023-08-17 12:08:49,102][131864] RunningMeanStd input shape: (3, 72, 128)
[2023-08-17 12:08:49,102][131864] RunningMeanStd input shape: (1,)
[2023-08-17 12:08:49,111][131864] ConvEncoder: input_channels=3
[2023-08-17 12:08:49,128][131877] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 12:08:49,128][131877] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
[2023-08-17 12:08:49,131][131877] Num visible devices: 1
[2023-08-17 12:08:49,176][131864] Conv encoder output size: 512
[2023-08-17 12:08:49,176][131864] Policy head output size: 512
[2023-08-17 12:08:49,183][131864] Created Actor Critic model with architecture:
[2023-08-17 12:08:49,183][131864] ActorCriticSharedWeights(
(obs_normalizer): ObservationNormalizer(
(running_mean_std): RunningMeanStdDictInPlace(
(running_mean_std): ModuleDict(
(obs): RunningMeanStdInPlace()
)
)
)
(returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
(encoder): VizdoomEncoder(
(basic_encoder): ConvEncoder(
(enc): RecursiveScriptModule(
original_name=ConvEncoderImpl
(conv_head): RecursiveScriptModule(
original_name=Sequential
(0): RecursiveScriptModule(original_name=Conv2d)
(1): RecursiveScriptModule(original_name=ELU)
(2): RecursiveScriptModule(original_name=Conv2d)
(3): RecursiveScriptModule(original_name=ELU)
(4): RecursiveScriptModule(original_name=Conv2d)
(5): RecursiveScriptModule(original_name=ELU)
)
(mlp_layers): RecursiveScriptModule(
original_name=Sequential
(0): RecursiveScriptModule(original_name=Linear)
(1): RecursiveScriptModule(original_name=ELU)
)
)
)
)
(core): ModelCoreRNN(
(core): GRU(512, 512)
)
(decoder): MlpDecoder(
(mlp): Identity()
)
(critic_linear): Linear(in_features=512, out_features=1, bias=True)
(action_parameterization): ActionParameterizationDefault(
(distribution_linear): Linear(in_features=512, out_features=5, bias=True)
)
)
[2023-08-17 12:08:49,230][131885] Worker 7 uses CPU cores [21, 22, 23]
[2023-08-17 12:08:49,237][131880] Worker 2 uses CPU cores [6, 7, 8]
[2023-08-17 12:08:49,239][131884] Worker 6 uses CPU cores [18, 19, 20]
[2023-08-17 12:08:49,240][131879] Worker 0 uses CPU cores [0, 1, 2]
[2023-08-17 12:08:49,241][131883] Worker 5 uses CPU cores [15, 16, 17]
[2023-08-17 12:08:49,242][131881] Worker 3 uses CPU cores [9, 10, 11]
[2023-08-17 12:08:49,247][131882] Worker 4 uses CPU cores [12, 13, 14]
[2023-08-17 12:08:49,280][131878] Worker 1 uses CPU cores [3, 4, 5]
[2023-08-17 12:08:50,303][131864] Using optimizer <class 'torch.optim.adam.Adam'>
[2023-08-17 12:08:50,303][131864] Loading state from checkpoint /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000170_696320.pth...
[2023-08-17 12:08:50,319][131864] Loading model from checkpoint
[2023-08-17 12:08:50,321][131864] Loaded experiment state at self.train_step=170, self.env_steps=696320
[2023-08-17 12:08:50,322][131864] Initialized policy 0 weights for model version 170
[2023-08-17 12:08:50,322][131864] LearnerWorker_p0 finished initialization!
[2023-08-17 12:08:50,323][131864] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 12:08:50,883][131877] RunningMeanStd input shape: (3, 72, 128)
[2023-08-17 12:08:50,883][131877] RunningMeanStd input shape: (1,)
[2023-08-17 12:08:50,890][131877] ConvEncoder: input_channels=3
[2023-08-17 12:08:50,941][131877] Conv encoder output size: 512
[2023-08-17 12:08:50,941][131877] Policy head output size: 512
[2023-08-17 12:08:51,065][131794] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 696320. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
[2023-08-17 12:08:51,490][131794] Inference worker 0-0 is ready!
[2023-08-17 12:08:51,491][131794] All inference workers are ready! Signal rollout workers to start!
[2023-08-17 12:08:51,506][131880] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:08:51,506][131884] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:08:51,506][131881] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:08:51,506][131885] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:08:51,508][131879] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:08:51,508][131878] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:08:51,508][131882] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:08:51,508][131883] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:08:51,714][131885] Decorrelating experience for 0 frames...
[2023-08-17 12:08:51,715][131883] Decorrelating experience for 0 frames...
[2023-08-17 12:08:51,715][131882] Decorrelating experience for 0 frames...
[2023-08-17 12:08:51,720][131881] Decorrelating experience for 0 frames...
[2023-08-17 12:08:51,735][131884] Decorrelating experience for 0 frames...
[2023-08-17 12:08:51,743][131880] Decorrelating experience for 0 frames...
[2023-08-17 12:08:51,900][131882] Decorrelating experience for 32 frames...
[2023-08-17 12:08:51,906][131885] Decorrelating experience for 32 frames...
[2023-08-17 12:08:51,906][131881] Decorrelating experience for 32 frames...
[2023-08-17 12:08:51,910][131878] Decorrelating experience for 0 frames...
[2023-08-17 12:08:51,920][131884] Decorrelating experience for 32 frames...
[2023-08-17 12:08:51,931][131880] Decorrelating experience for 32 frames...
[2023-08-17 12:08:51,956][131883] Decorrelating experience for 32 frames...
[2023-08-17 12:08:52,104][131878] Decorrelating experience for 32 frames...
[2023-08-17 12:08:52,108][131882] Decorrelating experience for 64 frames...
[2023-08-17 12:08:52,116][131879] Decorrelating experience for 0 frames...
[2023-08-17 12:08:52,124][131881] Decorrelating experience for 64 frames...
[2023-08-17 12:08:52,131][131884] Decorrelating experience for 64 frames...
[2023-08-17 12:08:52,182][131880] Decorrelating experience for 64 frames...
[2023-08-17 12:08:52,299][131878] Decorrelating experience for 64 frames...
[2023-08-17 12:08:52,301][131879] Decorrelating experience for 32 frames...
[2023-08-17 12:08:52,315][131885] Decorrelating experience for 64 frames...
[2023-08-17 12:08:52,441][131882] Decorrelating experience for 96 frames...
[2023-08-17 12:08:52,485][131884] Decorrelating experience for 96 frames...
[2023-08-17 12:08:52,502][131878] Decorrelating experience for 96 frames...
[2023-08-17 12:08:52,530][131879] Decorrelating experience for 64 frames...
[2023-08-17 12:08:52,680][131881] Decorrelating experience for 96 frames...
[2023-08-17 12:08:52,728][131883] Decorrelating experience for 64 frames...
[2023-08-17 12:08:52,732][131885] Decorrelating experience for 96 frames...
[2023-08-17 12:08:52,869][131879] Decorrelating experience for 96 frames...
[2023-08-17 12:08:52,919][131883] Decorrelating experience for 96 frames...
[2023-08-17 12:08:52,923][131880] Decorrelating experience for 96 frames...
[2023-08-17 12:08:53,218][131864] Signal inference workers to stop experience collection...
[2023-08-17 12:08:53,246][131877] InferenceWorker_p0-w0: stopping experience collection
[2023-08-17 12:08:54,097][131864] Signal inference workers to resume experience collection...
[2023-08-17 12:08:54,097][131877] InferenceWorker_p0-w0: resuming experience collection
[2023-08-17 12:08:55,323][131877] Updated weights for policy 0, policy_version 180 (0.0180)
[2023-08-17 12:08:56,065][131794] Fps is (10 sec: 13926.5, 60 sec: 13926.5, 300 sec: 13926.5). Total num frames: 765952. Throughput: 0: 662.8. Samples: 3314. Policy #0 lag: (min: 0.0, avg: 0.0, max: 0.0)
[2023-08-17 12:08:56,066][131794] Avg episode reward: [(0, '4.760')]
[2023-08-17 12:08:56,070][131864] Saving new best policy, reward=4.760!
[2023-08-17 12:08:56,305][131877] Updated weights for policy 0, policy_version 190 (0.0006)
[2023-08-17 12:08:57,286][131877] Updated weights for policy 0, policy_version 200 (0.0006)
[2023-08-17 12:08:58,252][131877] Updated weights for policy 0, policy_version 210 (0.0007)
[2023-08-17 12:08:59,223][131877] Updated weights for policy 0, policy_version 220 (0.0006)
[2023-08-17 12:09:00,213][131877] Updated weights for policy 0, policy_version 230 (0.0006)
[2023-08-17 12:09:01,065][131794] Fps is (10 sec: 27853.0, 60 sec: 27853.0, 300 sec: 27853.0). Total num frames: 974848. Throughput: 0: 6506.0. Samples: 65060. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 12:09:01,067][131794] Avg episode reward: [(0, '4.894')]
[2023-08-17 12:09:01,068][131864] Saving new best policy, reward=4.894!
[2023-08-17 12:09:01,199][131877] Updated weights for policy 0, policy_version 240 (0.0006)
[2023-08-17 12:09:02,167][131877] Updated weights for policy 0, policy_version 250 (0.0006)
[2023-08-17 12:09:03,131][131877] Updated weights for policy 0, policy_version 260 (0.0006)
[2023-08-17 12:09:04,168][131877] Updated weights for policy 0, policy_version 270 (0.0007)
[2023-08-17 12:09:05,171][131877] Updated weights for policy 0, policy_version 280 (0.0007)
[2023-08-17 12:09:06,065][131794] Fps is (10 sec: 41779.5, 60 sec: 32495.2, 300 sec: 32495.2). Total num frames: 1183744. Throughput: 0: 6420.3. Samples: 96304. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-08-17 12:09:06,066][131794] Avg episode reward: [(0, '5.072')]
[2023-08-17 12:09:06,069][131864] Saving new best policy, reward=5.072!
[2023-08-17 12:09:06,158][131877] Updated weights for policy 0, policy_version 290 (0.0007)
[2023-08-17 12:09:07,207][131877] Updated weights for policy 0, policy_version 300 (0.0006)
[2023-08-17 12:09:08,081][131794] Heartbeat connected on Batcher_0
[2023-08-17 12:09:08,084][131794] Heartbeat connected on LearnerWorker_p0
[2023-08-17 12:09:08,089][131794] Heartbeat connected on InferenceWorker_p0-w0
[2023-08-17 12:09:08,090][131794] Heartbeat connected on RolloutWorker_w0
[2023-08-17 12:09:08,093][131794] Heartbeat connected on RolloutWorker_w1
[2023-08-17 12:09:08,094][131794] Heartbeat connected on RolloutWorker_w2
[2023-08-17 12:09:08,097][131794] Heartbeat connected on RolloutWorker_w3
[2023-08-17 12:09:08,098][131794] Heartbeat connected on RolloutWorker_w4
[2023-08-17 12:09:08,102][131794] Heartbeat connected on RolloutWorker_w5
[2023-08-17 12:09:08,102][131794] Heartbeat connected on RolloutWorker_w6
[2023-08-17 12:09:08,105][131794] Heartbeat connected on RolloutWorker_w7
[2023-08-17 12:09:08,269][131877] Updated weights for policy 0, policy_version 310 (0.0007)
[2023-08-17 12:09:09,317][131877] Updated weights for policy 0, policy_version 320 (0.0007)
[2023-08-17 12:09:10,353][131877] Updated weights for policy 0, policy_version 330 (0.0006)
[2023-08-17 12:09:11,065][131794] Fps is (10 sec: 40140.7, 60 sec: 33996.9, 300 sec: 33996.9). Total num frames: 1376256. Throughput: 0: 7819.2. Samples: 156384. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
[2023-08-17 12:09:11,066][131794] Avg episode reward: [(0, '5.895')]
[2023-08-17 12:09:11,067][131864] Saving new best policy, reward=5.895!
[2023-08-17 12:09:11,382][131877] Updated weights for policy 0, policy_version 340 (0.0006)
[2023-08-17 12:09:12,356][131877] Updated weights for policy 0, policy_version 350 (0.0007)
[2023-08-17 12:09:13,358][131877] Updated weights for policy 0, policy_version 360 (0.0006)
[2023-08-17 12:09:14,367][131877] Updated weights for policy 0, policy_version 370 (0.0006)
[2023-08-17 12:09:15,414][131877] Updated weights for policy 0, policy_version 380 (0.0006)
[2023-08-17 12:09:16,065][131794] Fps is (10 sec: 39731.1, 60 sec: 35389.6, 300 sec: 35389.6). Total num frames: 1581056. Throughput: 0: 8668.8. Samples: 216718. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-08-17 12:09:16,066][131794] Avg episode reward: [(0, '5.889')]
[2023-08-17 12:09:16,442][131877] Updated weights for policy 0, policy_version 390 (0.0006)
[2023-08-17 12:09:17,456][131877] Updated weights for policy 0, policy_version 400 (0.0006)
[2023-08-17 12:09:18,507][131877] Updated weights for policy 0, policy_version 410 (0.0006)
[2023-08-17 12:09:19,530][131877] Updated weights for policy 0, policy_version 420 (0.0007)
[2023-08-17 12:09:20,560][131877] Updated weights for policy 0, policy_version 430 (0.0007)
[2023-08-17 12:09:21,065][131794] Fps is (10 sec: 40550.8, 60 sec: 36181.5, 300 sec: 36181.5). Total num frames: 1781760. Throughput: 0: 8212.2. Samples: 246364. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 12:09:21,066][131794] Avg episode reward: [(0, '6.038')]
[2023-08-17 12:09:21,066][131864] Saving new best policy, reward=6.038!
[2023-08-17 12:09:21,554][131877] Updated weights for policy 0, policy_version 440 (0.0006)
[2023-08-17 12:09:22,574][131877] Updated weights for policy 0, policy_version 450 (0.0007)
[2023-08-17 12:09:23,579][131877] Updated weights for policy 0, policy_version 460 (0.0006)
[2023-08-17 12:09:24,534][131877] Updated weights for policy 0, policy_version 470 (0.0006)
[2023-08-17 12:09:25,469][131877] Updated weights for policy 0, policy_version 480 (0.0006)
[2023-08-17 12:09:26,065][131794] Fps is (10 sec: 40960.0, 60 sec: 36981.1, 300 sec: 36981.1). Total num frames: 1990656. Throughput: 0: 8786.5. Samples: 307528. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 12:09:26,066][131794] Avg episode reward: [(0, '8.814')]
[2023-08-17 12:09:26,068][131864] Saving new best policy, reward=8.814!
[2023-08-17 12:09:26,467][131877] Updated weights for policy 0, policy_version 490 (0.0007)
[2023-08-17 12:09:27,472][131877] Updated weights for policy 0, policy_version 500 (0.0007)
[2023-08-17 12:09:28,481][131877] Updated weights for policy 0, policy_version 510 (0.0006)
[2023-08-17 12:09:29,497][131877] Updated weights for policy 0, policy_version 520 (0.0006)
[2023-08-17 12:09:30,455][131877] Updated weights for policy 0, policy_version 530 (0.0007)
[2023-08-17 12:09:31,065][131794] Fps is (10 sec: 40959.8, 60 sec: 37376.1, 300 sec: 37376.1). Total num frames: 2191360. Throughput: 0: 9247.0. Samples: 369878. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
[2023-08-17 12:09:31,066][131794] Avg episode reward: [(0, '10.505')]
[2023-08-17 12:09:31,067][131864] Saving new best policy, reward=10.505!
[2023-08-17 12:09:31,504][131877] Updated weights for policy 0, policy_version 540 (0.0006)
[2023-08-17 12:09:32,471][131877] Updated weights for policy 0, policy_version 550 (0.0006)
[2023-08-17 12:09:33,439][131877] Updated weights for policy 0, policy_version 560 (0.0006)
[2023-08-17 12:09:34,495][131877] Updated weights for policy 0, policy_version 570 (0.0007)
[2023-08-17 12:09:35,483][131877] Updated weights for policy 0, policy_version 580 (0.0006)
[2023-08-17 12:09:36,065][131794] Fps is (10 sec: 40550.4, 60 sec: 37774.3, 300 sec: 37774.3). Total num frames: 2396160. Throughput: 0: 8903.2. Samples: 400642. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-08-17 12:09:36,066][131794] Avg episode reward: [(0, '13.981')]
[2023-08-17 12:09:36,069][131864] Saving new best policy, reward=13.981!
[2023-08-17 12:09:36,506][131877] Updated weights for policy 0, policy_version 590 (0.0007)
[2023-08-17 12:09:37,541][131877] Updated weights for policy 0, policy_version 600 (0.0006)
[2023-08-17 12:09:38,535][131877] Updated weights for policy 0, policy_version 610 (0.0007)
[2023-08-17 12:09:39,574][131877] Updated weights for policy 0, policy_version 620 (0.0006)
[2023-08-17 12:09:40,629][131877] Updated weights for policy 0, policy_version 630 (0.0007)
[2023-08-17 12:09:41,065][131794] Fps is (10 sec: 40550.4, 60 sec: 38010.9, 300 sec: 38010.9). Total num frames: 2596864. Throughput: 0: 10165.2. Samples: 460746. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 12:09:41,066][131794] Avg episode reward: [(0, '16.720')]
[2023-08-17 12:09:41,067][131864] Saving new best policy, reward=16.720!
[2023-08-17 12:09:41,662][131877] Updated weights for policy 0, policy_version 640 (0.0006)
[2023-08-17 12:09:42,705][131877] Updated weights for policy 0, policy_version 650 (0.0007)
[2023-08-17 12:09:43,682][131877] Updated weights for policy 0, policy_version 660 (0.0006)
[2023-08-17 12:09:44,707][131877] Updated weights for policy 0, policy_version 670 (0.0007)
[2023-08-17 12:09:45,654][131877] Updated weights for policy 0, policy_version 680 (0.0006)
[2023-08-17 12:09:46,065][131794] Fps is (10 sec: 40550.4, 60 sec: 38279.1, 300 sec: 38279.1). Total num frames: 2801664. Throughput: 0: 10135.5. Samples: 521158. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-08-17 12:09:46,066][131794] Avg episode reward: [(0, '15.224')]
[2023-08-17 12:09:46,644][131877] Updated weights for policy 0, policy_version 690 (0.0006)
[2023-08-17 12:09:47,596][131877] Updated weights for policy 0, policy_version 700 (0.0006)
[2023-08-17 12:09:48,566][131877] Updated weights for policy 0, policy_version 710 (0.0006)
[2023-08-17 12:09:49,544][131877] Updated weights for policy 0, policy_version 720 (0.0006)
[2023-08-17 12:09:50,566][131877] Updated weights for policy 0, policy_version 730 (0.0007)
[2023-08-17 12:09:51,065][131794] Fps is (10 sec: 41369.5, 60 sec: 38570.7, 300 sec: 38570.7). Total num frames: 3010560. Throughput: 0: 10141.9. Samples: 552690. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 12:09:51,066][131794] Avg episode reward: [(0, '16.889')]
[2023-08-17 12:09:51,067][131864] Saving new best policy, reward=16.889!
[2023-08-17 12:09:51,497][131877] Updated weights for policy 0, policy_version 740 (0.0006)
[2023-08-17 12:09:52,458][131877] Updated weights for policy 0, policy_version 750 (0.0006)
[2023-08-17 12:09:53,420][131877] Updated weights for policy 0, policy_version 760 (0.0006)
[2023-08-17 12:09:54,449][131877] Updated weights for policy 0, policy_version 770 (0.0007)
[2023-08-17 12:09:55,425][131877] Updated weights for policy 0, policy_version 780 (0.0007)
[2023-08-17 12:09:56,065][131794] Fps is (10 sec: 41779.2, 60 sec: 40891.8, 300 sec: 38817.5). Total num frames: 3219456. Throughput: 0: 10201.7. Samples: 615458. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 12:09:56,066][131794] Avg episode reward: [(0, '18.774')]
[2023-08-17 12:09:56,069][131864] Saving new best policy, reward=18.774!
[2023-08-17 12:09:56,434][131877] Updated weights for policy 0, policy_version 790 (0.0006)
[2023-08-17 12:09:57,381][131877] Updated weights for policy 0, policy_version 800 (0.0007)
[2023-08-17 12:09:58,323][131877] Updated weights for policy 0, policy_version 810 (0.0006)
[2023-08-17 12:09:59,287][131877] Updated weights for policy 0, policy_version 820 (0.0006)
[2023-08-17 12:10:00,214][131877] Updated weights for policy 0, policy_version 830 (0.0006)
[2023-08-17 12:10:01,065][131794] Fps is (10 sec: 42188.9, 60 sec: 40960.0, 300 sec: 39087.6). Total num frames: 3432448. Throughput: 0: 10286.3. Samples: 679604. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 12:10:01,066][131794] Avg episode reward: [(0, '21.885')]
[2023-08-17 12:10:01,067][131864] Saving new best policy, reward=21.885!
[2023-08-17 12:10:01,201][131877] Updated weights for policy 0, policy_version 840 (0.0006)
[2023-08-17 12:10:02,187][131877] Updated weights for policy 0, policy_version 850 (0.0006)
[2023-08-17 12:10:03,190][131877] Updated weights for policy 0, policy_version 860 (0.0007)
[2023-08-17 12:10:04,150][131877] Updated weights for policy 0, policy_version 870 (0.0006)
[2023-08-17 12:10:05,158][131877] Updated weights for policy 0, policy_version 880 (0.0006)
[2023-08-17 12:10:06,065][131794] Fps is (10 sec: 41779.0, 60 sec: 40891.7, 300 sec: 39212.4). Total num frames: 3637248. Throughput: 0: 10318.1. Samples: 710680. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 12:10:06,066][131794] Avg episode reward: [(0, '19.364')]
[2023-08-17 12:10:06,176][131877] Updated weights for policy 0, policy_version 890 (0.0007)
[2023-08-17 12:10:07,147][131877] Updated weights for policy 0, policy_version 900 (0.0006)
[2023-08-17 12:10:08,101][131877] Updated weights for policy 0, policy_version 910 (0.0007)
[2023-08-17 12:10:09,146][131877] Updated weights for policy 0, policy_version 920 (0.0007)
[2023-08-17 12:10:10,146][131877] Updated weights for policy 0, policy_version 930 (0.0006)
[2023-08-17 12:10:11,065][131794] Fps is (10 sec: 41369.6, 60 sec: 41164.8, 300 sec: 39372.8). Total num frames: 3846144. Throughput: 0: 10327.7. Samples: 772274. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
[2023-08-17 12:10:11,066][131794] Avg episode reward: [(0, '19.007')]
[2023-08-17 12:10:11,109][131877] Updated weights for policy 0, policy_version 940 (0.0006)
[2023-08-17 12:10:12,081][131877] Updated weights for policy 0, policy_version 950 (0.0007)
[2023-08-17 12:10:13,110][131877] Updated weights for policy 0, policy_version 960 (0.0007)
[2023-08-17 12:10:14,089][131877] Updated weights for policy 0, policy_version 970 (0.0006)
[2023-08-17 12:10:14,877][131864] Saving /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-08-17 12:10:14,877][131794] Component Batcher_0 stopped!
[2023-08-17 12:10:14,877][131864] Stopping Batcher_0...
[2023-08-17 12:10:14,889][131864] Loop batcher_evt_loop terminating...
[2023-08-17 12:10:14,890][131877] Weights refcount: 2 0
[2023-08-17 12:10:14,891][131877] Stopping InferenceWorker_p0-w0...
[2023-08-17 12:10:14,891][131877] Loop inference_proc0-0_evt_loop terminating...
[2023-08-17 12:10:14,891][131794] Component InferenceWorker_p0-w0 stopped!
[2023-08-17 12:10:14,910][131864] Saving /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-08-17 12:10:14,930][131881] Stopping RolloutWorker_w3...
[2023-08-17 12:10:14,930][131881] Loop rollout_proc3_evt_loop terminating...
[2023-08-17 12:10:14,930][131794] Component RolloutWorker_w3 stopped!
[2023-08-17 12:10:14,935][131882] Stopping RolloutWorker_w4...
[2023-08-17 12:10:14,935][131878] Stopping RolloutWorker_w1...
[2023-08-17 12:10:14,935][131878] Loop rollout_proc1_evt_loop terminating...
[2023-08-17 12:10:14,935][131882] Loop rollout_proc4_evt_loop terminating...
[2023-08-17 12:10:14,935][131794] Component RolloutWorker_w4 stopped!
[2023-08-17 12:10:14,936][131884] Stopping RolloutWorker_w6...
[2023-08-17 12:10:14,936][131794] Component RolloutWorker_w1 stopped!
[2023-08-17 12:10:14,936][131884] Loop rollout_proc6_evt_loop terminating...
[2023-08-17 12:10:14,936][131794] Component RolloutWorker_w6 stopped!
[2023-08-17 12:10:14,938][131879] Stopping RolloutWorker_w0...
[2023-08-17 12:10:14,938][131879] Loop rollout_proc0_evt_loop terminating...
[2023-08-17 12:10:14,938][131794] Component RolloutWorker_w0 stopped!
[2023-08-17 12:10:14,940][131883] Stopping RolloutWorker_w5...
[2023-08-17 12:10:14,940][131794] Component RolloutWorker_w5 stopped!
[2023-08-17 12:10:14,940][131883] Loop rollout_proc5_evt_loop terminating...
[2023-08-17 12:10:14,940][131885] Stopping RolloutWorker_w7...
[2023-08-17 12:10:14,940][131885] Loop rollout_proc7_evt_loop terminating...
[2023-08-17 12:10:14,940][131794] Component RolloutWorker_w7 stopped!
[2023-08-17 12:10:14,963][131880] Stopping RolloutWorker_w2...
[2023-08-17 12:10:14,963][131880] Loop rollout_proc2_evt_loop terminating...
[2023-08-17 12:10:14,963][131794] Component RolloutWorker_w2 stopped!
[2023-08-17 12:10:14,973][131864] Stopping LearnerWorker_p0...
[2023-08-17 12:10:14,974][131864] Loop learner_proc0_evt_loop terminating...
[2023-08-17 12:10:14,974][131794] Component LearnerWorker_p0 stopped!
[2023-08-17 12:10:14,975][131794] Waiting for process learner_proc0 to stop...
[2023-08-17 12:10:15,620][131794] Waiting for process inference_proc0-0 to join...
[2023-08-17 12:10:15,620][131794] Waiting for process rollout_proc0 to join...
[2023-08-17 12:10:15,629][131794] Waiting for process rollout_proc1 to join...
[2023-08-17 12:10:15,630][131794] Waiting for process rollout_proc2 to join...
[2023-08-17 12:10:15,630][131794] Waiting for process rollout_proc3 to join...
[2023-08-17 12:10:15,631][131794] Waiting for process rollout_proc4 to join...
[2023-08-17 12:10:15,632][131794] Waiting for process rollout_proc5 to join...
[2023-08-17 12:10:15,632][131794] Waiting for process rollout_proc6 to join...
[2023-08-17 12:10:15,633][131794] Waiting for process rollout_proc7 to join...
[2023-08-17 12:10:15,633][131794] Batcher 0 profile tree view:
batching: 6.7047, releasing_batches: 0.0087
[2023-08-17 12:10:15,634][131794] InferenceWorker_p0-w0 profile tree view:
wait_policy: 0.0000
wait_policy_total: 2.0680
update_model: 1.3116
weight_update: 0.0007
one_step: 0.0012
handle_policy_step: 74.7597
deserialize: 3.1557, stack: 0.3403, obs_to_device_normalize: 17.0704, forward: 37.2802, send_messages: 4.8157
prepare_outputs: 8.7975
to_cpu: 5.6577
[2023-08-17 12:10:15,634][131794] Learner 0 profile tree view:
misc: 0.0028, prepare_batch: 4.0784
train: 9.7538
epoch_init: 0.0026, minibatch_init: 0.0026, losses_postprocess: 0.2418, kl_divergence: 0.1909, after_optimizer: 0.2499
calculate_losses: 3.4695
losses_init: 0.0014, forward_head: 0.2537, bptt_initial: 2.0322, tail: 0.2342, advantages_returns: 0.0568, losses: 0.4473
bptt: 0.3786
bptt_forward_core: 0.3600
update: 5.4474
clip: 2.7340
[2023-08-17 12:10:15,635][131794] RolloutWorker_w0 profile tree view:
wait_for_trajectories: 0.0615, enqueue_policy_requests: 2.6644, env_step: 37.2031, overhead: 3.5782, complete_rollouts: 0.0881
save_policy_outputs: 3.6697
split_output_tensors: 1.7077
[2023-08-17 12:10:15,635][131794] RolloutWorker_w7 profile tree view:
wait_for_trajectories: 0.0601, enqueue_policy_requests: 2.6081, env_step: 35.6438, overhead: 3.4090, complete_rollouts: 0.0854
save_policy_outputs: 3.4689
split_output_tensors: 1.6294
[2023-08-17 12:10:15,636][131794] Loop Runner_EvtLoop terminating...
[2023-08-17 12:10:15,637][131794] Runner profile tree view:
main_loop: 87.5323
[2023-08-17 12:10:15,637][131794] Collected {0: 4005888}, FPS: 37809.7
[2023-08-17 12:11:06,264][131794] Loading existing experiment configuration from /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/config.json
[2023-08-17 12:11:06,264][131794] Overriding arg 'num_workers' with value 1 passed from command line
[2023-08-17 12:11:06,265][131794] Adding new argument 'no_render'=True that is not in the saved config file!
[2023-08-17 12:11:06,265][131794] Adding new argument 'save_video'=True that is not in the saved config file!
[2023-08-17 12:11:06,265][131794] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
[2023-08-17 12:11:06,266][131794] Adding new argument 'video_name'=None that is not in the saved config file!
[2023-08-17 12:11:06,266][131794] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file!
[2023-08-17 12:11:06,266][131794] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
[2023-08-17 12:11:06,266][131794] Adding new argument 'push_to_hub'=False that is not in the saved config file!
[2023-08-17 12:11:06,267][131794] Adding new argument 'hf_repository'=None that is not in the saved config file!
[2023-08-17 12:11:06,267][131794] Adding new argument 'policy_index'=0 that is not in the saved config file!
[2023-08-17 12:11:06,267][131794] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
[2023-08-17 12:11:06,268][131794] Adding new argument 'train_script'=None that is not in the saved config file!
[2023-08-17 12:11:06,268][131794] Adding new argument 'enjoy_script'=None that is not in the saved config file!
[2023-08-17 12:11:06,268][131794] Using frameskip 1 and render_action_repeat=4 for evaluation
[2023-08-17 12:11:06,274][131794] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:11:06,275][131794] RunningMeanStd input shape: (3, 72, 128)
[2023-08-17 12:11:06,275][131794] RunningMeanStd input shape: (1,)
[2023-08-17 12:11:06,283][131794] ConvEncoder: input_channels=3
[2023-08-17 12:11:06,348][131794] Conv encoder output size: 512
[2023-08-17 12:11:06,349][131794] Policy head output size: 512
[2023-08-17 12:11:07,473][131794] Loading state from checkpoint /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-08-17 12:11:08,166][131794] Num frames 100...
[2023-08-17 12:11:08,225][131794] Num frames 200...
[2023-08-17 12:11:08,283][131794] Num frames 300...
[2023-08-17 12:11:08,341][131794] Num frames 400...
[2023-08-17 12:11:08,400][131794] Num frames 500...
[2023-08-17 12:11:08,457][131794] Num frames 600...
[2023-08-17 12:11:08,516][131794] Num frames 700...
[2023-08-17 12:11:08,574][131794] Num frames 800...
[2023-08-17 12:11:08,632][131794] Num frames 900...
[2023-08-17 12:11:08,694][131794] Num frames 1000...
[2023-08-17 12:11:08,753][131794] Num frames 1100...
[2023-08-17 12:11:08,811][131794] Num frames 1200...
[2023-08-17 12:11:08,869][131794] Num frames 1300...
[2023-08-17 12:11:08,928][131794] Num frames 1400...
[2023-08-17 12:11:09,003][131794] Avg episode rewards: #0: 31.400, true rewards: #0: 14.400
[2023-08-17 12:11:09,004][131794] Avg episode reward: 31.400, avg true_objective: 14.400
[2023-08-17 12:11:09,040][131794] Num frames 1500...
[2023-08-17 12:11:09,097][131794] Num frames 1600...
[2023-08-17 12:11:09,154][131794] Num frames 1700...
[2023-08-17 12:11:09,212][131794] Num frames 1800...
[2023-08-17 12:11:09,297][131794] Avg episode rewards: #0: 18.780, true rewards: #0: 9.280
[2023-08-17 12:11:09,298][131794] Avg episode reward: 18.780, avg true_objective: 9.280
[2023-08-17 12:11:09,323][131794] Num frames 1900...
[2023-08-17 12:11:09,382][131794] Num frames 2000...
[2023-08-17 12:11:09,439][131794] Num frames 2100...
[2023-08-17 12:11:09,498][131794] Num frames 2200...
[2023-08-17 12:11:09,574][131794] Avg episode rewards: #0: 14.467, true rewards: #0: 7.467
[2023-08-17 12:11:09,574][131794] Avg episode reward: 14.467, avg true_objective: 7.467
[2023-08-17 12:11:09,610][131794] Num frames 2300...
[2023-08-17 12:11:09,670][131794] Num frames 2400...
[2023-08-17 12:11:09,729][131794] Num frames 2500...
[2023-08-17 12:11:09,789][131794] Num frames 2600...
[2023-08-17 12:11:09,847][131794] Num frames 2700...
[2023-08-17 12:11:09,906][131794] Num frames 2800...
[2023-08-17 12:11:09,963][131794] Num frames 2900...
[2023-08-17 12:11:10,021][131794] Num frames 3000...
[2023-08-17 12:11:10,079][131794] Num frames 3100...
[2023-08-17 12:11:10,138][131794] Num frames 3200...
[2023-08-17 12:11:10,200][131794] Num frames 3300...
[2023-08-17 12:11:10,259][131794] Num frames 3400...
[2023-08-17 12:11:10,317][131794] Num frames 3500...
[2023-08-17 12:11:10,375][131794] Num frames 3600...
[2023-08-17 12:11:10,433][131794] Num frames 3700...
[2023-08-17 12:11:10,492][131794] Num frames 3800...
[2023-08-17 12:11:10,550][131794] Num frames 3900...
[2023-08-17 12:11:10,610][131794] Num frames 4000...
[2023-08-17 12:11:10,668][131794] Num frames 4100...
[2023-08-17 12:11:10,727][131794] Num frames 4200...
[2023-08-17 12:11:10,794][131794] Avg episode rewards: #0: 22.560, true rewards: #0: 10.560
[2023-08-17 12:11:10,794][131794] Avg episode reward: 22.560, avg true_objective: 10.560
[2023-08-17 12:11:10,840][131794] Num frames 4300...
[2023-08-17 12:11:10,898][131794] Num frames 4400...
[2023-08-17 12:11:10,959][131794] Num frames 4500...
[2023-08-17 12:11:11,017][131794] Num frames 4600...
[2023-08-17 12:11:11,076][131794] Num frames 4700...
[2023-08-17 12:11:11,134][131794] Num frames 4800...
[2023-08-17 12:11:11,242][131794] Avg episode rewards: #0: 20.392, true rewards: #0: 9.792
[2023-08-17 12:11:11,243][131794] Avg episode reward: 20.392, avg true_objective: 9.792
[2023-08-17 12:11:11,246][131794] Num frames 4900...
[2023-08-17 12:11:11,303][131794] Num frames 5000...
[2023-08-17 12:11:11,363][131794] Num frames 5100...
[2023-08-17 12:11:11,421][131794] Num frames 5200...
[2023-08-17 12:11:11,479][131794] Num frames 5300...
[2023-08-17 12:11:11,537][131794] Num frames 5400...
[2023-08-17 12:11:11,594][131794] Num frames 5500...
[2023-08-17 12:11:11,651][131794] Num frames 5600...
[2023-08-17 12:11:11,709][131794] Num frames 5700...
[2023-08-17 12:11:11,799][131794] Avg episode rewards: #0: 19.933, true rewards: #0: 9.600
[2023-08-17 12:11:11,799][131794] Avg episode reward: 19.933, avg true_objective: 9.600
[2023-08-17 12:11:11,823][131794] Num frames 5800...
[2023-08-17 12:11:11,881][131794] Num frames 5900...
[2023-08-17 12:11:11,940][131794] Num frames 6000...
[2023-08-17 12:11:11,998][131794] Num frames 6100...
[2023-08-17 12:11:12,056][131794] Num frames 6200...
[2023-08-17 12:11:12,113][131794] Num frames 6300...
[2023-08-17 12:11:12,170][131794] Num frames 6400...
[2023-08-17 12:11:12,260][131794] Avg episode rewards: #0: 19.234, true rewards: #0: 9.234
[2023-08-17 12:11:12,260][131794] Avg episode reward: 19.234, avg true_objective: 9.234
[2023-08-17 12:11:12,282][131794] Num frames 6500...
[2023-08-17 12:11:12,340][131794] Num frames 6600...
[2023-08-17 12:11:12,398][131794] Num frames 6700...
[2023-08-17 12:11:12,455][131794] Num frames 6800...
[2023-08-17 12:11:12,513][131794] Num frames 6900...
[2023-08-17 12:11:12,609][131794] Avg episode rewards: #0: 17.720, true rewards: #0: 8.720
[2023-08-17 12:11:12,609][131794] Avg episode reward: 17.720, avg true_objective: 8.720
[2023-08-17 12:11:12,624][131794] Num frames 7000...
[2023-08-17 12:11:12,683][131794] Num frames 7100...
[2023-08-17 12:11:12,757][131794] Avg episode rewards: #0: 16.040, true rewards: #0: 7.929
[2023-08-17 12:11:12,757][131794] Avg episode reward: 16.040, avg true_objective: 7.929
[2023-08-17 12:11:12,794][131794] Num frames 7200...
[2023-08-17 12:11:12,852][131794] Num frames 7300...
[2023-08-17 12:11:12,910][131794] Num frames 7400...
[2023-08-17 12:11:12,968][131794] Num frames 7500...
[2023-08-17 12:11:13,027][131794] Num frames 7600...
[2023-08-17 12:11:13,084][131794] Num frames 7700...
[2023-08-17 12:11:13,142][131794] Num frames 7800...
[2023-08-17 12:11:13,200][131794] Num frames 7900...
[2023-08-17 12:11:13,298][131794] Avg episode rewards: #0: 16.278, true rewards: #0: 7.978
[2023-08-17 12:11:13,299][131794] Avg episode reward: 16.278, avg true_objective: 7.978
[2023-08-17 12:11:20,895][131794] Replay video saved to /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/replay.mp4!
[2023-08-17 12:52:38,482][131794] Loading existing experiment configuration from /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/config.json
[2023-08-17 12:52:38,483][131794] Overriding arg 'num_workers' with value 1 passed from command line
[2023-08-17 12:52:38,483][131794] Adding new argument 'no_render'=True that is not in the saved config file!
[2023-08-17 12:52:38,483][131794] Adding new argument 'save_video'=True that is not in the saved config file!
[2023-08-17 12:52:38,484][131794] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
[2023-08-17 12:52:38,484][131794] Adding new argument 'video_name'=None that is not in the saved config file!
[2023-08-17 12:52:38,485][131794] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!
[2023-08-17 12:52:38,485][131794] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
[2023-08-17 12:52:38,486][131794] Adding new argument 'push_to_hub'=True that is not in the saved config file!
[2023-08-17 12:52:38,486][131794] Adding new argument 'hf_repository'='patonw/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file!
[2023-08-17 12:52:38,486][131794] Adding new argument 'policy_index'=0 that is not in the saved config file!
[2023-08-17 12:52:38,487][131794] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
[2023-08-17 12:52:38,487][131794] Adding new argument 'train_script'=None that is not in the saved config file!
[2023-08-17 12:52:38,487][131794] Adding new argument 'enjoy_script'=None that is not in the saved config file!
[2023-08-17 12:52:38,488][131794] Using frameskip 1 and render_action_repeat=4 for evaluation
[2023-08-17 12:52:38,491][131794] RunningMeanStd input shape: (3, 72, 128)
[2023-08-17 12:52:38,492][131794] RunningMeanStd input shape: (1,)
[2023-08-17 12:52:38,497][131794] ConvEncoder: input_channels=3
[2023-08-17 12:52:38,518][131794] Conv encoder output size: 512
[2023-08-17 12:52:38,519][131794] Policy head output size: 512
[2023-08-17 12:52:38,534][131794] Loading state from checkpoint /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-08-17 12:52:38,832][131794] Num frames 100...
[2023-08-17 12:52:38,889][131794] Num frames 200...
[2023-08-17 12:52:38,952][131794] Num frames 300...
[2023-08-17 12:52:39,009][131794] Num frames 400...
[2023-08-17 12:52:39,065][131794] Num frames 500...
[2023-08-17 12:52:39,122][131794] Num frames 600...
[2023-08-17 12:52:39,178][131794] Num frames 700...
[2023-08-17 12:52:39,237][131794] Num frames 800...
[2023-08-17 12:52:39,293][131794] Num frames 900...
[2023-08-17 12:52:39,349][131794] Num frames 1000...
[2023-08-17 12:52:39,406][131794] Num frames 1100...
[2023-08-17 12:52:39,462][131794] Num frames 1200...
[2023-08-17 12:52:39,520][131794] Num frames 1300...
[2023-08-17 12:52:39,576][131794] Num frames 1400...
[2023-08-17 12:52:39,633][131794] Num frames 1500...
[2023-08-17 12:52:39,691][131794] Num frames 1600...
[2023-08-17 12:52:39,748][131794] Num frames 1700...
[2023-08-17 12:52:39,804][131794] Num frames 1800...
[2023-08-17 12:52:39,860][131794] Num frames 1900...
[2023-08-17 12:52:39,917][131794] Num frames 2000...
[2023-08-17 12:52:39,997][131794] Avg episode rewards: #0: 47.479, true rewards: #0: 20.480
[2023-08-17 12:52:39,998][131794] Avg episode reward: 47.479, avg true_objective: 20.480
[2023-08-17 12:52:40,027][131794] Num frames 2100...
[2023-08-17 12:52:40,084][131794] Num frames 2200...
[2023-08-17 12:52:40,142][131794] Num frames 2300...
[2023-08-17 12:52:40,199][131794] Num frames 2400...
[2023-08-17 12:52:40,256][131794] Num frames 2500...
[2023-08-17 12:52:40,312][131794] Num frames 2600...
[2023-08-17 12:52:40,368][131794] Num frames 2700...
[2023-08-17 12:52:40,424][131794] Num frames 2800...
[2023-08-17 12:52:40,522][131794] Avg episode rewards: #0: 30.900, true rewards: #0: 14.400
[2023-08-17 12:52:40,523][131794] Avg episode reward: 30.900, avg true_objective: 14.400
[2023-08-17 12:52:40,535][131794] Num frames 2900...
[2023-08-17 12:52:40,591][131794] Num frames 3000...
[2023-08-17 12:52:40,648][131794] Num frames 3100...
[2023-08-17 12:52:40,704][131794] Num frames 3200...
[2023-08-17 12:52:40,760][131794] Num frames 3300...
[2023-08-17 12:52:40,815][131794] Num frames 3400...
[2023-08-17 12:52:40,899][131794] Avg episode rewards: #0: 23.853, true rewards: #0: 11.520
[2023-08-17 12:52:40,899][131794] Avg episode reward: 23.853, avg true_objective: 11.520
[2023-08-17 12:52:40,925][131794] Num frames 3500...
[2023-08-17 12:52:40,981][131794] Num frames 3600...
[2023-08-17 12:52:41,039][131794] Num frames 3700...
[2023-08-17 12:52:41,096][131794] Num frames 3800...
[2023-08-17 12:52:41,153][131794] Num frames 3900...
[2023-08-17 12:52:41,211][131794] Num frames 4000...
[2023-08-17 12:52:41,270][131794] Num frames 4100...
[2023-08-17 12:52:41,328][131794] Num frames 4200...
[2023-08-17 12:52:41,386][131794] Num frames 4300...
[2023-08-17 12:52:41,446][131794] Num frames 4400...
[2023-08-17 12:52:41,525][131794] Avg episode rewards: #0: 22.870, true rewards: #0: 11.120
[2023-08-17 12:52:41,526][131794] Avg episode reward: 22.870, avg true_objective: 11.120
[2023-08-17 12:52:41,555][131794] Num frames 4500...
[2023-08-17 12:52:41,612][131794] Num frames 4600...
[2023-08-17 12:52:41,668][131794] Num frames 4700...
[2023-08-17 12:52:41,725][131794] Num frames 4800...
[2023-08-17 12:52:41,781][131794] Num frames 4900...
[2023-08-17 12:52:41,868][131794] Avg episode rewards: #0: 20.322, true rewards: #0: 9.922
[2023-08-17 12:52:41,869][131794] Avg episode reward: 20.322, avg true_objective: 9.922
[2023-08-17 12:52:41,891][131794] Num frames 5000...
[2023-08-17 12:52:41,948][131794] Num frames 5100...
[2023-08-17 12:52:42,004][131794] Num frames 5200...
[2023-08-17 12:52:42,063][131794] Num frames 5300...
[2023-08-17 12:52:42,119][131794] Num frames 5400...
[2023-08-17 12:52:42,175][131794] Num frames 5500...
[2023-08-17 12:52:42,231][131794] Num frames 5600...
[2023-08-17 12:52:42,287][131794] Num frames 5700...
[2023-08-17 12:52:42,344][131794] Num frames 5800...
[2023-08-17 12:52:42,400][131794] Num frames 5900...
[2023-08-17 12:52:42,456][131794] Num frames 6000...
[2023-08-17 12:52:42,513][131794] Num frames 6100...
[2023-08-17 12:52:42,567][131794] Num frames 6200...
[2023-08-17 12:52:42,621][131794] Num frames 6300...
[2023-08-17 12:52:42,675][131794] Num frames 6400...
[2023-08-17 12:52:42,762][131794] Avg episode rewards: #0: 22.108, true rewards: #0: 10.775
[2023-08-17 12:52:42,763][131794] Avg episode reward: 22.108, avg true_objective: 10.775
[2023-08-17 12:52:42,781][131794] Num frames 6500...
[2023-08-17 12:52:42,836][131794] Num frames 6600...
[2023-08-17 12:52:42,889][131794] Num frames 6700...
[2023-08-17 12:52:42,944][131794] Num frames 6800...
[2023-08-17 12:52:42,997][131794] Num frames 6900...
[2023-08-17 12:52:43,051][131794] Num frames 7000...
[2023-08-17 12:52:43,107][131794] Num frames 7100...
[2023-08-17 12:52:43,162][131794] Num frames 7200...
[2023-08-17 12:52:43,217][131794] Num frames 7300...
[2023-08-17 12:52:43,285][131794] Avg episode rewards: #0: 21.751, true rewards: #0: 10.466
[2023-08-17 12:52:43,285][131794] Avg episode reward: 21.751, avg true_objective: 10.466
[2023-08-17 12:52:43,325][131794] Num frames 7400...
[2023-08-17 12:52:43,381][131794] Num frames 7500...
[2023-08-17 12:52:43,435][131794] Num frames 7600...
[2023-08-17 12:52:43,490][131794] Num frames 7700...
[2023-08-17 12:52:43,544][131794] Num frames 7800...
[2023-08-17 12:52:43,602][131794] Num frames 7900...
[2023-08-17 12:52:43,657][131794] Num frames 8000...
[2023-08-17 12:52:43,711][131794] Num frames 8100...
[2023-08-17 12:52:43,766][131794] Num frames 8200...
[2023-08-17 12:52:43,824][131794] Num frames 8300...
[2023-08-17 12:52:43,881][131794] Num frames 8400...
[2023-08-17 12:52:43,959][131794] Avg episode rewards: #0: 22.183, true rewards: #0: 10.557
[2023-08-17 12:52:43,960][131794] Avg episode reward: 22.183, avg true_objective: 10.557
[2023-08-17 12:52:43,991][131794] Num frames 8500...
[2023-08-17 12:52:44,048][131794] Num frames 8600...
[2023-08-17 12:52:44,105][131794] Num frames 8700...
[2023-08-17 12:52:44,162][131794] Num frames 8800...
[2023-08-17 12:52:44,218][131794] Num frames 8900...
[2023-08-17 12:52:44,272][131794] Num frames 9000...
[2023-08-17 12:52:44,328][131794] Num frames 9100...
[2023-08-17 12:52:44,383][131794] Num frames 9200...
[2023-08-17 12:52:44,437][131794] Num frames 9300...
[2023-08-17 12:52:44,492][131794] Num frames 9400...
[2023-08-17 12:52:44,548][131794] Avg episode rewards: #0: 22.229, true rewards: #0: 10.451
[2023-08-17 12:52:44,549][131794] Avg episode reward: 22.229, avg true_objective: 10.451
[2023-08-17 12:52:44,599][131794] Num frames 9500...
[2023-08-17 12:52:44,655][131794] Num frames 9600...
[2023-08-17 12:52:44,709][131794] Num frames 9700...
[2023-08-17 12:52:44,763][131794] Num frames 9800...
[2023-08-17 12:52:44,835][131794] Avg episode rewards: #0: 20.835, true rewards: #0: 9.835
[2023-08-17 12:52:44,835][131794] Avg episode reward: 20.835, avg true_objective: 9.835
[2023-08-17 12:52:54,306][131794] Replay video saved to /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/replay.mp4!
[2023-08-17 12:54:20,339][131794] The model has been pushed to https://huggingface.co/patonw/rl_course_vizdoom_health_gathering_supreme
[2023-08-17 12:59:09,422][131794] Environment doom_basic already registered, overwriting...
[2023-08-17 12:59:09,423][131794] Environment doom_two_colors_easy already registered, overwriting...
[2023-08-17 12:59:09,423][131794] Environment doom_two_colors_hard already registered, overwriting...
[2023-08-17 12:59:09,424][131794] Environment doom_dm already registered, overwriting...
[2023-08-17 12:59:09,424][131794] Environment doom_dwango5 already registered, overwriting...
[2023-08-17 12:59:09,424][131794] Environment doom_my_way_home_flat_actions already registered, overwriting...
[2023-08-17 12:59:09,425][131794] Environment doom_defend_the_center_flat_actions already registered, overwriting...
[2023-08-17 12:59:09,425][131794] Environment doom_my_way_home already registered, overwriting...
[2023-08-17 12:59:09,425][131794] Environment doom_deadly_corridor already registered, overwriting...
[2023-08-17 12:59:09,425][131794] Environment doom_defend_the_center already registered, overwriting...
[2023-08-17 12:59:09,426][131794] Environment doom_defend_the_line already registered, overwriting...
[2023-08-17 12:59:09,426][131794] Environment doom_health_gathering already registered, overwriting...
[2023-08-17 12:59:09,426][131794] Environment doom_health_gathering_supreme already registered, overwriting...
[2023-08-17 12:59:09,427][131794] Environment doom_battle already registered, overwriting...
[2023-08-17 12:59:09,427][131794] Environment doom_battle2 already registered, overwriting...
[2023-08-17 12:59:09,427][131794] Environment doom_duel_bots already registered, overwriting...
[2023-08-17 12:59:09,427][131794] Environment doom_deathmatch_bots already registered, overwriting...
[2023-08-17 12:59:09,428][131794] Environment doom_duel already registered, overwriting...
[2023-08-17 12:59:09,428][131794] Environment doom_deathmatch_full already registered, overwriting...
[2023-08-17 12:59:09,428][131794] Environment doom_benchmark already registered, overwriting...
[2023-08-17 12:59:09,429][131794] register_encoder_factory: <function make_vizdoom_encoder at 0x7f0223bcab90>
[2023-08-17 12:59:29,604][131794] Environment doom_basic already registered, overwriting...
[2023-08-17 12:59:29,605][131794] Environment doom_two_colors_easy already registered, overwriting...
[2023-08-17 12:59:29,606][131794] Environment doom_two_colors_hard already registered, overwriting...
[2023-08-17 12:59:29,606][131794] Environment doom_dm already registered, overwriting...
[2023-08-17 12:59:29,606][131794] Environment doom_dwango5 already registered, overwriting...
[2023-08-17 12:59:29,607][131794] Environment doom_my_way_home_flat_actions already registered, overwriting...
[2023-08-17 12:59:29,607][131794] Environment doom_defend_the_center_flat_actions already registered, overwriting...
[2023-08-17 12:59:29,607][131794] Environment doom_my_way_home already registered, overwriting...
[2023-08-17 12:59:29,608][131794] Environment doom_deadly_corridor already registered, overwriting...
[2023-08-17 12:59:29,608][131794] Environment doom_defend_the_center already registered, overwriting...
[2023-08-17 12:59:29,608][131794] Environment doom_defend_the_line already registered, overwriting...
[2023-08-17 12:59:29,609][131794] Environment doom_health_gathering already registered, overwriting...
[2023-08-17 12:59:29,609][131794] Environment doom_health_gathering_supreme already registered, overwriting...
[2023-08-17 12:59:29,609][131794] Environment doom_battle already registered, overwriting...
[2023-08-17 12:59:29,610][131794] Environment doom_battle2 already registered, overwriting...
[2023-08-17 12:59:29,610][131794] Environment doom_duel_bots already registered, overwriting...
[2023-08-17 12:59:29,611][131794] Environment doom_deathmatch_bots already registered, overwriting...
[2023-08-17 12:59:29,611][131794] Environment doom_duel already registered, overwriting...
[2023-08-17 12:59:29,611][131794] Environment doom_deathmatch_full already registered, overwriting...
[2023-08-17 12:59:29,612][131794] Environment doom_benchmark already registered, overwriting...
[2023-08-17 12:59:29,612][131794] register_encoder_factory: <function make_vizdoom_encoder at 0x7f0223bcab90>
[2023-08-17 12:59:29,630][131794] Loading existing experiment configuration from /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/config.json
[2023-08-17 12:59:29,631][131794] Overriding arg 'train_for_env_steps' with value 10000000 passed from command line
[2023-08-17 12:59:29,631][131794] Overriding arg 'with_wandb' with value True passed from command line
[2023-08-17 12:59:29,634][131794] Experiment dir /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment already exists!
[2023-08-17 12:59:29,635][131794] Resuming existing experiment from /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment...
[2023-08-17 12:59:29,635][131794] Weights and Biases integration enabled. Project: sample_factory, user: None, group: None, unique_id: default_experiment_20230817_125929_635646
[2023-08-17 12:59:29,819][131794] Initializing WandB...
[2023-08-17 12:59:34,788][131794] Environment var CUDA_VISIBLE_DEVICES is 0
[2023-08-17 12:59:35,803][131794] Starting experiment with the following configuration:
help=False
algo=APPO
env=doom_health_gathering_supreme
experiment=default_experiment
train_dir=/home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir
restart_behavior=resume
device=gpu
seed=None
num_policies=1
async_rl=True
serial_mode=False
batched_sampling=False
num_batches_to_accumulate=2
worker_num_splits=2
policy_workers_per_policy=1
max_policy_lag=1000
num_workers=8
num_envs_per_worker=4
batch_size=1024
num_batches_per_epoch=1
num_epochs=1
rollout=32
recurrence=32
shuffle_minibatches=False
gamma=0.99
reward_scale=1.0
reward_clip=1000.0
value_bootstrap=False
normalize_returns=True
exploration_loss_coeff=0.001
value_loss_coeff=0.5
kl_loss_coeff=0.0
exploration_loss=symmetric_kl
gae_lambda=0.95
ppo_clip_ratio=0.1
ppo_clip_value=0.2
with_vtrace=False
vtrace_rho=1.0
vtrace_c=1.0
optimizer=adam
adam_eps=1e-06
adam_beta1=0.9
adam_beta2=0.999
max_grad_norm=4.0
learning_rate=0.0001
lr_schedule=constant
lr_schedule_kl_threshold=0.008
lr_adaptive_min=1e-06
lr_adaptive_max=0.01
obs_subtract_mean=0.0
obs_scale=255.0
normalize_input=True
normalize_input_keys=None
decorrelate_experience_max_seconds=0
decorrelate_envs_on_one_worker=True
actor_worker_gpus=[]
set_workers_cpu_affinity=True
force_envs_single_thread=False
default_niceness=0
log_to_file=True
experiment_summaries_interval=10
flush_summaries_interval=30
stats_avg=100
summaries_use_frameskip=True
heartbeat_interval=20
heartbeat_reporting_interval=600
train_for_env_steps=10000000
train_for_seconds=10000000000
save_every_sec=120
keep_checkpoints=2
load_checkpoint_kind=latest
save_milestones_sec=-1
save_best_every_sec=5
save_best_metric=reward
save_best_after=100000
benchmark=False
encoder_mlp_layers=[512, 512]
encoder_conv_architecture=convnet_simple
encoder_conv_mlp_layers=[512]
use_rnn=True
rnn_size=512
rnn_type=gru
rnn_num_layers=1
decoder_mlp_layers=[]
nonlinearity=elu
policy_initialization=orthogonal
policy_init_gain=1.0
actor_critic_share_weights=True
adaptive_stddev=True
continuous_tanh_scale=0.0
initial_stddev=1.0
use_env_info_cache=False
env_gpu_actions=False
env_gpu_observations=True
env_frameskip=4
env_framestack=1
pixel_format=CHW
use_record_episode_statistics=False
with_wandb=True
wandb_user=None
wandb_project=sample_factory
wandb_group=None
wandb_job_type=SF
wandb_tags=[]
with_pbt=False
pbt_mix_policies_in_one_env=True
pbt_period_env_steps=5000000
pbt_start_mutation=20000000
pbt_replace_fraction=0.3
pbt_mutation_rate=0.15
pbt_replace_reward_gap=0.1
pbt_replace_reward_gap_absolute=1e-06
pbt_optimize_gamma=False
pbt_target_objective=true_objective
pbt_perturb_min=1.1
pbt_perturb_max=1.5
num_agents=-1
num_humans=0
num_bots=-1
start_bot_difficulty=None
timelimit=None
res_w=128
res_h=72
wide_aspect_ratio=False
eval_env_frameskip=1
fps=35
command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
git_hash=336df5a551fea3a2cf40925bf3083db6b4518c91
git_repo_name=https://github.com/huggingface/deep-rl-class
wandb_unique_id=default_experiment_20230817_125929_635646
[2023-08-17 12:59:35,804][131794] Saving configuration to /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/config.json...
[2023-08-17 12:59:35,879][131794] Rollout worker 0 uses device cpu
[2023-08-17 12:59:35,879][131794] Rollout worker 1 uses device cpu
[2023-08-17 12:59:35,880][131794] Rollout worker 2 uses device cpu
[2023-08-17 12:59:35,881][131794] Rollout worker 3 uses device cpu
[2023-08-17 12:59:35,881][131794] Rollout worker 4 uses device cpu
[2023-08-17 12:59:35,882][131794] Rollout worker 5 uses device cpu
[2023-08-17 12:59:35,882][131794] Rollout worker 6 uses device cpu
[2023-08-17 12:59:35,883][131794] Rollout worker 7 uses device cpu
[2023-08-17 12:59:35,911][131794] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 12:59:35,912][131794] InferenceWorker_p0-w0: min num requests: 2
[2023-08-17 12:59:35,935][131794] Starting all processes...
[2023-08-17 12:59:35,936][131794] Starting process learner_proc0
[2023-08-17 12:59:35,984][131794] Starting all processes...
[2023-08-17 12:59:35,986][131794] Starting process inference_proc0-0
[2023-08-17 12:59:35,986][131794] Starting process rollout_proc0
[2023-08-17 12:59:35,986][131794] Starting process rollout_proc1
[2023-08-17 12:59:35,986][131794] Starting process rollout_proc2
[2023-08-17 12:59:35,987][131794] Starting process rollout_proc3
[2023-08-17 12:59:35,987][131794] Starting process rollout_proc4
[2023-08-17 12:59:35,988][131794] Starting process rollout_proc5
[2023-08-17 12:59:35,988][131794] Starting process rollout_proc6
[2023-08-17 12:59:35,989][131794] Starting process rollout_proc7
[2023-08-17 12:59:36,953][138062] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 12:59:36,953][138062] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
[2023-08-17 12:59:36,957][138062] Num visible devices: 1
[2023-08-17 12:59:36,973][138062] Starting seed is not provided
[2023-08-17 12:59:36,974][138062] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 12:59:36,974][138062] Initializing actor-critic model on device cuda:0
[2023-08-17 12:59:36,974][138062] RunningMeanStd input shape: (3, 72, 128)
[2023-08-17 12:59:36,975][138062] RunningMeanStd input shape: (1,)
[2023-08-17 12:59:36,983][138062] ConvEncoder: input_channels=3
[2023-08-17 12:59:37,000][138076] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 12:59:37,000][138076] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
[2023-08-17 12:59:37,004][138076] Num visible devices: 1
[2023-08-17 12:59:37,043][138062] Conv encoder output size: 512
[2023-08-17 12:59:37,043][138062] Policy head output size: 512
[2023-08-17 12:59:37,048][138077] Worker 1 uses CPU cores [3, 4, 5]
[2023-08-17 12:59:37,050][138062] Created Actor Critic model with architecture:
[2023-08-17 12:59:37,051][138062] ActorCriticSharedWeights(
(obs_normalizer): ObservationNormalizer(
(running_mean_std): RunningMeanStdDictInPlace(
(running_mean_std): ModuleDict(
(obs): RunningMeanStdInPlace()
)
)
)
(returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
(encoder): VizdoomEncoder(
(basic_encoder): ConvEncoder(
(enc): RecursiveScriptModule(
original_name=ConvEncoderImpl
(conv_head): RecursiveScriptModule(
original_name=Sequential
(0): RecursiveScriptModule(original_name=Conv2d)
(1): RecursiveScriptModule(original_name=ELU)
(2): RecursiveScriptModule(original_name=Conv2d)
(3): RecursiveScriptModule(original_name=ELU)
(4): RecursiveScriptModule(original_name=Conv2d)
(5): RecursiveScriptModule(original_name=ELU)
)
(mlp_layers): RecursiveScriptModule(
original_name=Sequential
(0): RecursiveScriptModule(original_name=Linear)
(1): RecursiveScriptModule(original_name=ELU)
)
)
)
)
(core): ModelCoreRNN(
(core): GRU(512, 512)
)
(decoder): MlpDecoder(
(mlp): Identity()
)
(critic_linear): Linear(in_features=512, out_features=1, bias=True)
(action_parameterization): ActionParameterizationDefault(
(distribution_linear): Linear(in_features=512, out_features=5, bias=True)
)
)
[2023-08-17 12:59:37,051][138075] Worker 0 uses CPU cores [0, 1, 2]
[2023-08-17 12:59:37,061][138081] Worker 5 uses CPU cores [15, 16, 17]
[2023-08-17 12:59:37,061][138082] Worker 6 uses CPU cores [18, 19, 20]
[2023-08-17 12:59:37,061][138083] Worker 7 uses CPU cores [21, 22, 23]
[2023-08-17 12:59:37,065][138078] Worker 2 uses CPU cores [6, 7, 8]
[2023-08-17 12:59:37,075][138079] Worker 3 uses CPU cores [9, 10, 11]
[2023-08-17 12:59:37,079][138080] Worker 4 uses CPU cores [12, 13, 14]
[2023-08-17 12:59:37,161][138062] Using optimizer <class 'torch.optim.adam.Adam'>
[2023-08-17 12:59:37,161][138062] Loading state from checkpoint /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-08-17 12:59:37,184][138062] Loading model from checkpoint
[2023-08-17 12:59:37,187][138062] Loaded experiment state at self.train_step=978, self.env_steps=4005888
[2023-08-17 12:59:37,187][138062] Initialized policy 0 weights for model version 978
[2023-08-17 12:59:37,188][138062] LearnerWorker_p0 finished initialization!
[2023-08-17 12:59:37,188][138062] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-08-17 12:59:37,231][138076] RunningMeanStd input shape: (3, 72, 128)
[2023-08-17 12:59:37,232][138076] RunningMeanStd input shape: (1,)
[2023-08-17 12:59:37,238][138076] ConvEncoder: input_channels=3
[2023-08-17 12:59:37,290][138076] Conv encoder output size: 512
[2023-08-17 12:59:37,290][138076] Policy head output size: 512
[2023-08-17 12:59:37,315][131794] Inference worker 0-0 is ready!
[2023-08-17 12:59:37,316][131794] All inference workers are ready! Signal rollout workers to start!
[2023-08-17 12:59:37,333][138083] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:59:37,334][138079] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:59:37,334][138080] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:59:37,334][138075] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:59:37,335][138077] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:59:37,335][138081] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:59:37,335][138078] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:59:37,336][138082] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-08-17 12:59:37,546][138075] Decorrelating experience for 0 frames...
[2023-08-17 12:59:37,547][138083] Decorrelating experience for 0 frames...
[2023-08-17 12:59:37,557][138077] Decorrelating experience for 0 frames...
[2023-08-17 12:59:37,734][138083] Decorrelating experience for 32 frames...
[2023-08-17 12:59:37,734][138079] Decorrelating experience for 0 frames...
[2023-08-17 12:59:37,734][138078] Decorrelating experience for 0 frames...
[2023-08-17 12:59:37,740][138077] Decorrelating experience for 32 frames...
[2023-08-17 12:59:37,785][138075] Decorrelating experience for 32 frames...
[2023-08-17 12:59:37,920][138078] Decorrelating experience for 32 frames...
[2023-08-17 12:59:37,941][138083] Decorrelating experience for 64 frames...
[2023-08-17 12:59:37,941][138080] Decorrelating experience for 0 frames...
[2023-08-17 12:59:37,951][138077] Decorrelating experience for 64 frames...
[2023-08-17 12:59:37,959][138079] Decorrelating experience for 32 frames...
[2023-08-17 12:59:38,127][138078] Decorrelating experience for 64 frames...
[2023-08-17 12:59:38,138][138080] Decorrelating experience for 32 frames...
[2023-08-17 12:59:38,145][138075] Decorrelating experience for 64 frames...
[2023-08-17 12:59:38,164][138083] Decorrelating experience for 96 frames...
[2023-08-17 12:59:38,164][138077] Decorrelating experience for 96 frames...
[2023-08-17 12:59:38,172][138079] Decorrelating experience for 64 frames...
[2023-08-17 12:59:38,335][138078] Decorrelating experience for 96 frames...
[2023-08-17 12:59:38,375][138081] Decorrelating experience for 0 frames...
[2023-08-17 12:59:38,386][138075] Decorrelating experience for 96 frames...
[2023-08-17 12:59:38,408][138079] Decorrelating experience for 96 frames...
[2023-08-17 12:59:38,420][138080] Decorrelating experience for 64 frames...
[2023-08-17 12:59:38,628][138082] Decorrelating experience for 0 frames...
[2023-08-17 12:59:38,630][138081] Decorrelating experience for 32 frames...
[2023-08-17 12:59:38,750][138062] Signal inference workers to stop experience collection...
[2023-08-17 12:59:38,756][138076] InferenceWorker_p0-w0: stopping experience collection
[2023-08-17 12:59:38,821][138082] Decorrelating experience for 32 frames...
[2023-08-17 12:59:38,835][138081] Decorrelating experience for 64 frames...
[2023-08-17 12:59:38,836][138080] Decorrelating experience for 96 frames...
[2023-08-17 12:59:39,025][138082] Decorrelating experience for 64 frames...
[2023-08-17 12:59:39,042][138081] Decorrelating experience for 96 frames...
[2023-08-17 12:59:39,218][138082] Decorrelating experience for 96 frames...
[2023-08-17 12:59:39,282][138062] Signal inference workers to resume experience collection...
[2023-08-17 12:59:39,283][138076] InferenceWorker_p0-w0: resuming experience collection
[2023-08-17 12:59:39,788][131794] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 4014080. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: 0.0, avg: 0.0, max: 0.0)
[2023-08-17 12:59:39,789][131794] Avg episode reward: [(0, '6.493')]
[2023-08-17 12:59:40,549][138076] Updated weights for policy 0, policy_version 988 (0.0184)
[2023-08-17 12:59:41,527][138076] Updated weights for policy 0, policy_version 998 (0.0006)
[2023-08-17 12:59:42,585][138076] Updated weights for policy 0, policy_version 1008 (0.0006)
[2023-08-17 12:59:43,648][138076] Updated weights for policy 0, policy_version 1018 (0.0007)
[2023-08-17 12:59:44,652][138076] Updated weights for policy 0, policy_version 1028 (0.0006)
[2023-08-17 12:59:44,788][131794] Fps is (10 sec: 40141.5, 60 sec: 40141.5, 300 sec: 40141.5). Total num frames: 4214784. Throughput: 0: 7708.9. Samples: 38544. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 12:59:44,789][131794] Avg episode reward: [(0, '20.021')]
[2023-08-17 12:59:45,583][138076] Updated weights for policy 0, policy_version 1038 (0.0006)
[2023-08-17 12:59:46,560][138076] Updated weights for policy 0, policy_version 1048 (0.0006)
[2023-08-17 12:59:47,593][138076] Updated weights for policy 0, policy_version 1058 (0.0006)
[2023-08-17 12:59:48,603][138076] Updated weights for policy 0, policy_version 1068 (0.0006)
[2023-08-17 12:59:49,605][138076] Updated weights for policy 0, policy_version 1078 (0.0006)
[2023-08-17 12:59:49,788][131794] Fps is (10 sec: 40550.4, 60 sec: 40550.4, 300 sec: 40550.4). Total num frames: 4419584. Throughput: 0: 10010.8. Samples: 100108. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
[2023-08-17 12:59:49,789][131794] Avg episode reward: [(0, '21.241')]
[2023-08-17 12:59:50,586][138076] Updated weights for policy 0, policy_version 1088 (0.0006)
[2023-08-17 12:59:51,564][138076] Updated weights for policy 0, policy_version 1098 (0.0006)
[2023-08-17 12:59:52,582][138076] Updated weights for policy 0, policy_version 1108 (0.0006)
[2023-08-17 12:59:53,540][138076] Updated weights for policy 0, policy_version 1118 (0.0006)
[2023-08-17 12:59:54,551][138076] Updated weights for policy 0, policy_version 1128 (0.0006)
[2023-08-17 12:59:54,788][131794] Fps is (10 sec: 41369.6, 60 sec: 40960.3, 300 sec: 40960.3). Total num frames: 4628480. Throughput: 0: 8754.6. Samples: 131318. Policy #0 lag: (min: 0.0, avg: 0.8, max: 1.0)
[2023-08-17 12:59:54,789][131794] Avg episode reward: [(0, '19.294')]
[2023-08-17 12:59:55,528][138076] Updated weights for policy 0, policy_version 1138 (0.0006)
[2023-08-17 12:59:55,905][131794] Heartbeat connected on Batcher_0
[2023-08-17 12:59:55,915][131794] Heartbeat connected on LearnerWorker_p0
[2023-08-17 12:59:55,916][131794] Heartbeat connected on InferenceWorker_p0-w0
[2023-08-17 12:59:55,917][131794] Heartbeat connected on RolloutWorker_w0
[2023-08-17 12:59:55,919][131794] Heartbeat connected on RolloutWorker_w1
[2023-08-17 12:59:55,922][131794] Heartbeat connected on RolloutWorker_w2
[2023-08-17 12:59:55,925][131794] Heartbeat connected on RolloutWorker_w3
[2023-08-17 12:59:55,928][131794] Heartbeat connected on RolloutWorker_w4
[2023-08-17 12:59:55,929][131794] Heartbeat connected on RolloutWorker_w5
[2023-08-17 12:59:55,932][131794] Heartbeat connected on RolloutWorker_w6
[2023-08-17 12:59:55,934][131794] Heartbeat connected on RolloutWorker_w7
[2023-08-17 12:59:56,515][138076] Updated weights for policy 0, policy_version 1148 (0.0006)
[2023-08-17 12:59:57,482][138076] Updated weights for policy 0, policy_version 1158 (0.0006)
[2023-08-17 12:59:58,514][138076] Updated weights for policy 0, policy_version 1168 (0.0006)
[2023-08-17 12:59:59,555][138076] Updated weights for policy 0, policy_version 1178 (0.0006)
[2023-08-17 12:59:59,788][131794] Fps is (10 sec: 41369.8, 60 sec: 40960.1, 300 sec: 40960.1). Total num frames: 4833280. Throughput: 0: 9661.5. Samples: 193230. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 12:59:59,789][131794] Avg episode reward: [(0, '25.107')]
[2023-08-17 12:59:59,790][138062] Saving new best policy, reward=25.107!
[2023-08-17 13:00:00,580][138076] Updated weights for policy 0, policy_version 1188 (0.0006)
[2023-08-17 13:00:01,571][138076] Updated weights for policy 0, policy_version 1198 (0.0006)
[2023-08-17 13:00:02,515][138076] Updated weights for policy 0, policy_version 1208 (0.0006)
[2023-08-17 13:00:03,491][138076] Updated weights for policy 0, policy_version 1218 (0.0006)
[2023-08-17 13:00:04,455][138076] Updated weights for policy 0, policy_version 1228 (0.0006)
[2023-08-17 13:00:04,788][131794] Fps is (10 sec: 41369.4, 60 sec: 41123.9, 300 sec: 41123.9). Total num frames: 5042176. Throughput: 0: 10216.7. Samples: 255418. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:00:04,789][131794] Avg episode reward: [(0, '23.711')]
[2023-08-17 13:00:05,441][138076] Updated weights for policy 0, policy_version 1238 (0.0006)
[2023-08-17 13:00:06,429][138076] Updated weights for policy 0, policy_version 1248 (0.0006)
[2023-08-17 13:00:07,404][138076] Updated weights for policy 0, policy_version 1258 (0.0006)
[2023-08-17 13:00:08,373][138076] Updated weights for policy 0, policy_version 1268 (0.0006)
[2023-08-17 13:00:09,421][138076] Updated weights for policy 0, policy_version 1278 (0.0006)
[2023-08-17 13:00:09,788][131794] Fps is (10 sec: 41369.8, 60 sec: 41096.7, 300 sec: 41096.7). Total num frames: 5246976. Throughput: 0: 9549.6. Samples: 286486. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:00:09,789][131794] Avg episode reward: [(0, '20.401')]
[2023-08-17 13:00:10,433][138076] Updated weights for policy 0, policy_version 1288 (0.0007)
[2023-08-17 13:00:11,471][138076] Updated weights for policy 0, policy_version 1298 (0.0007)
[2023-08-17 13:00:12,500][138076] Updated weights for policy 0, policy_version 1308 (0.0006)
[2023-08-17 13:00:13,520][138076] Updated weights for policy 0, policy_version 1318 (0.0006)
[2023-08-17 13:00:14,499][138076] Updated weights for policy 0, policy_version 1328 (0.0006)
[2023-08-17 13:00:14,788][131794] Fps is (10 sec: 40550.6, 60 sec: 40960.1, 300 sec: 40960.1). Total num frames: 5447680. Throughput: 0: 9908.0. Samples: 346780. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:00:14,789][131794] Avg episode reward: [(0, '21.480')]
[2023-08-17 13:00:15,502][138076] Updated weights for policy 0, policy_version 1338 (0.0006)
[2023-08-17 13:00:16,525][138076] Updated weights for policy 0, policy_version 1348 (0.0007)
[2023-08-17 13:00:17,539][138076] Updated weights for policy 0, policy_version 1358 (0.0006)
[2023-08-17 13:00:18,483][138076] Updated weights for policy 0, policy_version 1368 (0.0006)
[2023-08-17 13:00:19,457][138076] Updated weights for policy 0, policy_version 1378 (0.0006)
[2023-08-17 13:00:19,788][131794] Fps is (10 sec: 40959.7, 60 sec: 41062.4, 300 sec: 41062.4). Total num frames: 5656576. Throughput: 0: 10223.4. Samples: 408936. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:00:19,789][131794] Avg episode reward: [(0, '24.208')]
[2023-08-17 13:00:20,437][138076] Updated weights for policy 0, policy_version 1388 (0.0006)
[2023-08-17 13:00:21,479][138076] Updated weights for policy 0, policy_version 1398 (0.0007)
[2023-08-17 13:00:22,461][138076] Updated weights for policy 0, policy_version 1408 (0.0007)
[2023-08-17 13:00:23,400][138076] Updated weights for policy 0, policy_version 1418 (0.0006)
[2023-08-17 13:00:24,390][138076] Updated weights for policy 0, policy_version 1428 (0.0006)
[2023-08-17 13:00:24,788][131794] Fps is (10 sec: 41779.0, 60 sec: 41142.1, 300 sec: 41142.1). Total num frames: 5865472. Throughput: 0: 9769.6. Samples: 439632. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:00:24,789][131794] Avg episode reward: [(0, '23.461')]
[2023-08-17 13:00:25,374][138076] Updated weights for policy 0, policy_version 1438 (0.0006)
[2023-08-17 13:00:26,415][138076] Updated weights for policy 0, policy_version 1448 (0.0007)
[2023-08-17 13:00:27,435][138076] Updated weights for policy 0, policy_version 1458 (0.0006)
[2023-08-17 13:00:28,377][138076] Updated weights for policy 0, policy_version 1468 (0.0006)
[2023-08-17 13:00:29,386][138076] Updated weights for policy 0, policy_version 1478 (0.0007)
[2023-08-17 13:00:29,788][131794] Fps is (10 sec: 41369.8, 60 sec: 41123.9, 300 sec: 41123.9). Total num frames: 6070272. Throughput: 0: 10296.5. Samples: 501888. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:00:29,789][131794] Avg episode reward: [(0, '23.247')]
[2023-08-17 13:00:30,352][138076] Updated weights for policy 0, policy_version 1488 (0.0006)
[2023-08-17 13:00:31,330][138076] Updated weights for policy 0, policy_version 1498 (0.0006)
[2023-08-17 13:00:32,335][138076] Updated weights for policy 0, policy_version 1508 (0.0006)
[2023-08-17 13:00:33,303][138076] Updated weights for policy 0, policy_version 1518 (0.0006)
[2023-08-17 13:00:34,304][138076] Updated weights for policy 0, policy_version 1528 (0.0006)
[2023-08-17 13:00:34,788][131794] Fps is (10 sec: 41369.8, 60 sec: 41183.5, 300 sec: 41183.5). Total num frames: 6279168. Throughput: 0: 10313.4. Samples: 564208. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:00:34,789][131794] Avg episode reward: [(0, '21.354')]
[2023-08-17 13:00:35,279][138076] Updated weights for policy 0, policy_version 1538 (0.0006)
[2023-08-17 13:00:36,307][138076] Updated weights for policy 0, policy_version 1548 (0.0007)
[2023-08-17 13:00:37,355][138076] Updated weights for policy 0, policy_version 1558 (0.0007)
[2023-08-17 13:00:38,294][138076] Updated weights for policy 0, policy_version 1568 (0.0007)
[2023-08-17 13:00:39,268][138076] Updated weights for policy 0, policy_version 1578 (0.0006)
[2023-08-17 13:00:39,788][131794] Fps is (10 sec: 41369.6, 60 sec: 41164.9, 300 sec: 41164.9). Total num frames: 6483968. Throughput: 0: 10290.4. Samples: 594388. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
[2023-08-17 13:00:39,789][131794] Avg episode reward: [(0, '20.492')]
[2023-08-17 13:00:40,270][138076] Updated weights for policy 0, policy_version 1588 (0.0007)
[2023-08-17 13:00:41,245][138076] Updated weights for policy 0, policy_version 1598 (0.0006)
[2023-08-17 13:00:42,195][138076] Updated weights for policy 0, policy_version 1608 (0.0006)
[2023-08-17 13:00:43,197][138076] Updated weights for policy 0, policy_version 1618 (0.0006)
[2023-08-17 13:00:44,192][138076] Updated weights for policy 0, policy_version 1628 (0.0007)
[2023-08-17 13:00:44,788][131794] Fps is (10 sec: 41369.3, 60 sec: 41301.3, 300 sec: 41212.1). Total num frames: 6692864. Throughput: 0: 10311.1. Samples: 657232. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:00:44,789][131794] Avg episode reward: [(0, '22.781')]
[2023-08-17 13:00:45,189][138076] Updated weights for policy 0, policy_version 1638 (0.0006)
[2023-08-17 13:00:46,149][138076] Updated weights for policy 0, policy_version 1648 (0.0006)
[2023-08-17 13:00:47,125][138076] Updated weights for policy 0, policy_version 1658 (0.0006)
[2023-08-17 13:00:48,117][138076] Updated weights for policy 0, policy_version 1668 (0.0006)
[2023-08-17 13:00:49,070][138076] Updated weights for policy 0, policy_version 1678 (0.0006)
[2023-08-17 13:00:49,788][131794] Fps is (10 sec: 41778.5, 60 sec: 41369.5, 300 sec: 41252.5). Total num frames: 6901760. Throughput: 0: 10331.5. Samples: 720336. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-08-17 13:00:49,789][131794] Avg episode reward: [(0, '23.809')]
[2023-08-17 13:00:49,994][138076] Updated weights for policy 0, policy_version 1688 (0.0006)
[2023-08-17 13:00:50,994][138076] Updated weights for policy 0, policy_version 1698 (0.0007)
[2023-08-17 13:00:51,961][138076] Updated weights for policy 0, policy_version 1708 (0.0006)
[2023-08-17 13:00:52,902][138076] Updated weights for policy 0, policy_version 1718 (0.0006)
[2023-08-17 13:00:53,878][138076] Updated weights for policy 0, policy_version 1728 (0.0007)
[2023-08-17 13:00:54,788][131794] Fps is (10 sec: 41779.3, 60 sec: 41369.6, 300 sec: 41287.7). Total num frames: 7110656. Throughput: 0: 10348.9. Samples: 752188. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-08-17 13:00:54,789][131794] Avg episode reward: [(0, '20.533')]
[2023-08-17 13:00:54,908][138076] Updated weights for policy 0, policy_version 1738 (0.0007)
[2023-08-17 13:00:55,862][138076] Updated weights for policy 0, policy_version 1748 (0.0006)
[2023-08-17 13:00:56,857][138076] Updated weights for policy 0, policy_version 1758 (0.0006)
[2023-08-17 13:00:57,828][138076] Updated weights for policy 0, policy_version 1768 (0.0006)
[2023-08-17 13:00:58,775][138076] Updated weights for policy 0, policy_version 1778 (0.0006)
[2023-08-17 13:00:59,711][138076] Updated weights for policy 0, policy_version 1788 (0.0006)
[2023-08-17 13:00:59,788][131794] Fps is (10 sec: 42189.4, 60 sec: 41506.1, 300 sec: 41369.6). Total num frames: 7323648. Throughput: 0: 10401.9. Samples: 814868. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:00:59,789][131794] Avg episode reward: [(0, '24.820')]
[2023-08-17 13:01:00,668][138076] Updated weights for policy 0, policy_version 1798 (0.0007)
[2023-08-17 13:01:01,641][138076] Updated weights for policy 0, policy_version 1808 (0.0006)
[2023-08-17 13:01:02,611][138076] Updated weights for policy 0, policy_version 1818 (0.0006)
[2023-08-17 13:01:03,492][138076] Updated weights for policy 0, policy_version 1828 (0.0005)
[2023-08-17 13:01:04,409][138076] Updated weights for policy 0, policy_version 1838 (0.0006)
[2023-08-17 13:01:04,788][131794] Fps is (10 sec: 43417.8, 60 sec: 41711.0, 300 sec: 41538.3). Total num frames: 7544832. Throughput: 0: 10472.4. Samples: 880194. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:01:04,789][131794] Avg episode reward: [(0, '21.285')]
[2023-08-17 13:01:05,381][138076] Updated weights for policy 0, policy_version 1848 (0.0006)
[2023-08-17 13:01:06,393][138076] Updated weights for policy 0, policy_version 1858 (0.0006)
[2023-08-17 13:01:07,365][138076] Updated weights for policy 0, policy_version 1868 (0.0006)
[2023-08-17 13:01:08,372][138076] Updated weights for policy 0, policy_version 1878 (0.0006)
[2023-08-17 13:01:09,387][138076] Updated weights for policy 0, policy_version 1888 (0.0006)
[2023-08-17 13:01:09,788][131794] Fps is (10 sec: 42188.8, 60 sec: 41642.6, 300 sec: 41460.6). Total num frames: 7745536. Throughput: 0: 10490.1. Samples: 911686. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-08-17 13:01:09,789][131794] Avg episode reward: [(0, '23.546')]
[2023-08-17 13:01:10,409][138076] Updated weights for policy 0, policy_version 1898 (0.0006)
[2023-08-17 13:01:11,405][138076] Updated weights for policy 0, policy_version 1908 (0.0006)
[2023-08-17 13:01:12,388][138076] Updated weights for policy 0, policy_version 1918 (0.0006)
[2023-08-17 13:01:13,419][138076] Updated weights for policy 0, policy_version 1928 (0.0006)
[2023-08-17 13:01:14,389][138076] Updated weights for policy 0, policy_version 1938 (0.0006)
[2023-08-17 13:01:14,788][131794] Fps is (10 sec: 40959.9, 60 sec: 41779.2, 300 sec: 41477.4). Total num frames: 7954432. Throughput: 0: 10458.9. Samples: 972538. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-08-17 13:01:14,789][131794] Avg episode reward: [(0, '22.888')]
[2023-08-17 13:01:15,393][138076] Updated weights for policy 0, policy_version 1948 (0.0006)
[2023-08-17 13:01:16,338][138076] Updated weights for policy 0, policy_version 1958 (0.0006)
[2023-08-17 13:01:17,292][138076] Updated weights for policy 0, policy_version 1968 (0.0005)
[2023-08-17 13:01:18,289][138076] Updated weights for policy 0, policy_version 1978 (0.0006)
[2023-08-17 13:01:19,274][138076] Updated weights for policy 0, policy_version 1988 (0.0006)
[2023-08-17 13:01:19,788][131794] Fps is (10 sec: 41779.1, 60 sec: 41779.2, 300 sec: 41492.5). Total num frames: 8163328. Throughput: 0: 10474.6. Samples: 1035566. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:01:19,789][131794] Avg episode reward: [(0, '23.391')]
[2023-08-17 13:01:20,269][138076] Updated weights for policy 0, policy_version 1998 (0.0006)
[2023-08-17 13:01:21,317][138076] Updated weights for policy 0, policy_version 2008 (0.0007)
[2023-08-17 13:01:22,285][138076] Updated weights for policy 0, policy_version 2018 (0.0006)
[2023-08-17 13:01:23,339][138076] Updated weights for policy 0, policy_version 2028 (0.0007)
[2023-08-17 13:01:24,378][138076] Updated weights for policy 0, policy_version 2038 (0.0007)
[2023-08-17 13:01:24,788][131794] Fps is (10 sec: 40549.8, 60 sec: 41574.3, 300 sec: 41389.1). Total num frames: 8359936. Throughput: 0: 10485.4. Samples: 1066232. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
[2023-08-17 13:01:24,789][131794] Avg episode reward: [(0, '24.663')]
[2023-08-17 13:01:25,431][138076] Updated weights for policy 0, policy_version 2048 (0.0007)
[2023-08-17 13:01:26,428][138076] Updated weights for policy 0, policy_version 2058 (0.0006)
[2023-08-17 13:01:27,401][138076] Updated weights for policy 0, policy_version 2068 (0.0006)
[2023-08-17 13:01:28,336][138076] Updated weights for policy 0, policy_version 2078 (0.0007)
[2023-08-17 13:01:29,286][138076] Updated weights for policy 0, policy_version 2088 (0.0006)
[2023-08-17 13:01:29,788][131794] Fps is (10 sec: 40550.2, 60 sec: 41642.6, 300 sec: 41406.8). Total num frames: 8568832. Throughput: 0: 10439.8. Samples: 1127022. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
[2023-08-17 13:01:29,790][131794] Avg episode reward: [(0, '25.178')]
[2023-08-17 13:01:29,791][138062] Saving new best policy, reward=25.178!
[2023-08-17 13:01:30,320][138076] Updated weights for policy 0, policy_version 2098 (0.0007)
[2023-08-17 13:01:31,287][138076] Updated weights for policy 0, policy_version 2108 (0.0006)
[2023-08-17 13:01:32,279][138076] Updated weights for policy 0, policy_version 2118 (0.0006)
[2023-08-17 13:01:33,314][138076] Updated weights for policy 0, policy_version 2128 (0.0007)
[2023-08-17 13:01:34,294][138076] Updated weights for policy 0, policy_version 2138 (0.0006)
[2023-08-17 13:01:34,788][131794] Fps is (10 sec: 41370.4, 60 sec: 41574.4, 300 sec: 41387.4). Total num frames: 8773632. Throughput: 0: 10409.6. Samples: 1188768. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:01:34,789][131794] Avg episode reward: [(0, '26.292')]
[2023-08-17 13:01:34,791][138062] Saving /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000002142_8773632.pth...
[2023-08-17 13:01:34,827][138062] Removing /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000170_696320.pth
[2023-08-17 13:01:34,834][138062] Saving new best policy, reward=26.292!
[2023-08-17 13:01:35,358][138076] Updated weights for policy 0, policy_version 2148 (0.0007)
[2023-08-17 13:01:36,310][138076] Updated weights for policy 0, policy_version 2158 (0.0006)
[2023-08-17 13:01:37,294][138076] Updated weights for policy 0, policy_version 2168 (0.0006)
[2023-08-17 13:01:38,264][138076] Updated weights for policy 0, policy_version 2178 (0.0006)
[2023-08-17 13:01:39,278][138076] Updated weights for policy 0, policy_version 2188 (0.0007)
[2023-08-17 13:01:39,788][131794] Fps is (10 sec: 41370.0, 60 sec: 41642.7, 300 sec: 41403.8). Total num frames: 8982528. Throughput: 0: 10381.0. Samples: 1219334. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-08-17 13:01:39,789][131794] Avg episode reward: [(0, '21.438')]
[2023-08-17 13:01:40,280][138076] Updated weights for policy 0, policy_version 2198 (0.0006)
[2023-08-17 13:01:41,229][138076] Updated weights for policy 0, policy_version 2208 (0.0006)
[2023-08-17 13:01:42,216][138076] Updated weights for policy 0, policy_version 2218 (0.0006)
[2023-08-17 13:01:43,186][138076] Updated weights for policy 0, policy_version 2228 (0.0006)
[2023-08-17 13:01:44,149][138076] Updated weights for policy 0, policy_version 2238 (0.0006)
[2023-08-17 13:01:44,788][131794] Fps is (10 sec: 41779.0, 60 sec: 41642.7, 300 sec: 41418.8). Total num frames: 9191424. Throughput: 0: 10379.3. Samples: 1281936. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-08-17 13:01:44,789][131794] Avg episode reward: [(0, '22.403')]
[2023-08-17 13:01:45,098][138076] Updated weights for policy 0, policy_version 2248 (0.0006)
[2023-08-17 13:01:46,042][138076] Updated weights for policy 0, policy_version 2258 (0.0006)
[2023-08-17 13:01:47,015][138076] Updated weights for policy 0, policy_version 2268 (0.0006)
[2023-08-17 13:01:47,992][138076] Updated weights for policy 0, policy_version 2278 (0.0007)
[2023-08-17 13:01:49,037][138076] Updated weights for policy 0, policy_version 2288 (0.0007)
[2023-08-17 13:01:49,788][131794] Fps is (10 sec: 41779.1, 60 sec: 41642.8, 300 sec: 41432.6). Total num frames: 9400320. Throughput: 0: 10329.8. Samples: 1345036. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-08-17 13:01:49,789][131794] Avg episode reward: [(0, '24.155')]
[2023-08-17 13:01:50,078][138076] Updated weights for policy 0, policy_version 2298 (0.0006)
[2023-08-17 13:01:51,011][138076] Updated weights for policy 0, policy_version 2308 (0.0006)
[2023-08-17 13:01:52,009][138076] Updated weights for policy 0, policy_version 2318 (0.0006)
[2023-08-17 13:01:52,933][138076] Updated weights for policy 0, policy_version 2328 (0.0006)
[2023-08-17 13:01:53,988][138076] Updated weights for policy 0, policy_version 2338 (0.0007)
[2023-08-17 13:01:54,788][131794] Fps is (10 sec: 41779.5, 60 sec: 41642.7, 300 sec: 41445.5). Total num frames: 9609216. Throughput: 0: 10323.1. Samples: 1376224. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-08-17 13:01:54,789][131794] Avg episode reward: [(0, '23.394')]
[2023-08-17 13:01:54,969][138076] Updated weights for policy 0, policy_version 2348 (0.0006)
[2023-08-17 13:01:55,990][138076] Updated weights for policy 0, policy_version 2358 (0.0006)
[2023-08-17 13:01:56,962][138076] Updated weights for policy 0, policy_version 2368 (0.0006)
[2023-08-17 13:01:57,986][138076] Updated weights for policy 0, policy_version 2378 (0.0006)
[2023-08-17 13:01:58,980][138076] Updated weights for policy 0, policy_version 2388 (0.0006)
[2023-08-17 13:01:59,788][131794] Fps is (10 sec: 41369.7, 60 sec: 41506.1, 300 sec: 41428.1). Total num frames: 9814016. Throughput: 0: 10345.8. Samples: 1438100. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
[2023-08-17 13:01:59,789][131794] Avg episode reward: [(0, '22.972')]
[2023-08-17 13:01:59,927][138076] Updated weights for policy 0, policy_version 2398 (0.0006)
[2023-08-17 13:02:00,931][138076] Updated weights for policy 0, policy_version 2408 (0.0007)
[2023-08-17 13:02:01,923][138076] Updated weights for policy 0, policy_version 2418 (0.0006)
[2023-08-17 13:02:02,877][138076] Updated weights for policy 0, policy_version 2428 (0.0005)
[2023-08-17 13:02:03,905][138076] Updated weights for policy 0, policy_version 2438 (0.0007)
[2023-08-17 13:02:04,439][138062] Stopping Batcher_0...
[2023-08-17 13:02:04,440][138062] Loop batcher_evt_loop terminating...
[2023-08-17 13:02:04,440][138062] Saving /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000002443_10006528.pth...
[2023-08-17 13:02:04,439][131794] Component Batcher_0 stopped!
[2023-08-17 13:02:04,455][138076] Weights refcount: 2 0
[2023-08-17 13:02:04,456][138076] Stopping InferenceWorker_p0-w0...
[2023-08-17 13:02:04,456][138076] Loop inference_proc0-0_evt_loop terminating...
[2023-08-17 13:02:04,456][131794] Component InferenceWorker_p0-w0 stopped!
[2023-08-17 13:02:04,473][138062] Removing /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth
[2023-08-17 13:02:04,477][138062] Saving /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000002443_10006528.pth...
[2023-08-17 13:02:04,501][138080] Stopping RolloutWorker_w4...
[2023-08-17 13:02:04,501][138080] Loop rollout_proc4_evt_loop terminating...
[2023-08-17 13:02:04,501][131794] Component RolloutWorker_w4 stopped!
[2023-08-17 13:02:04,507][138081] Stopping RolloutWorker_w5...
[2023-08-17 13:02:04,507][138079] Stopping RolloutWorker_w3...
[2023-08-17 13:02:04,507][138081] Loop rollout_proc5_evt_loop terminating...
[2023-08-17 13:02:04,507][138079] Loop rollout_proc3_evt_loop terminating...
[2023-08-17 13:02:04,507][131794] Component RolloutWorker_w3 stopped!
[2023-08-17 13:02:04,508][131794] Component RolloutWorker_w5 stopped!
[2023-08-17 13:02:04,511][138083] Stopping RolloutWorker_w7...
[2023-08-17 13:02:04,511][138083] Loop rollout_proc7_evt_loop terminating...
[2023-08-17 13:02:04,511][131794] Component RolloutWorker_w7 stopped!
[2023-08-17 13:02:04,512][138078] Stopping RolloutWorker_w2...
[2023-08-17 13:02:04,512][138078] Loop rollout_proc2_evt_loop terminating...
[2023-08-17 13:02:04,513][138082] Stopping RolloutWorker_w6...
[2023-08-17 13:02:04,512][131794] Component RolloutWorker_w2 stopped!
[2023-08-17 13:02:04,513][138082] Loop rollout_proc6_evt_loop terminating...
[2023-08-17 13:02:04,513][131794] Component RolloutWorker_w6 stopped!
[2023-08-17 13:02:04,519][138077] Stopping RolloutWorker_w1...
[2023-08-17 13:02:04,519][138077] Loop rollout_proc1_evt_loop terminating...
[2023-08-17 13:02:04,519][131794] Component RolloutWorker_w1 stopped!
[2023-08-17 13:02:04,535][138062] Stopping LearnerWorker_p0...
[2023-08-17 13:02:04,536][138062] Loop learner_proc0_evt_loop terminating...
[2023-08-17 13:02:04,535][131794] Component LearnerWorker_p0 stopped!
[2023-08-17 13:02:04,539][138075] Stopping RolloutWorker_w0...
[2023-08-17 13:02:04,540][138075] Loop rollout_proc0_evt_loop terminating...
[2023-08-17 13:02:04,539][131794] Component RolloutWorker_w0 stopped!
[2023-08-17 13:02:04,540][131794] Waiting for process learner_proc0 to stop...
[2023-08-17 13:02:05,188][131794] Waiting for process inference_proc0-0 to join...
[2023-08-17 13:02:05,189][131794] Waiting for process rollout_proc0 to join...
[2023-08-17 13:02:05,190][131794] Waiting for process rollout_proc1 to join...
[2023-08-17 13:02:05,190][131794] Waiting for process rollout_proc2 to join...
[2023-08-17 13:02:05,191][131794] Waiting for process rollout_proc3 to join...
[2023-08-17 13:02:05,191][131794] Waiting for process rollout_proc4 to join...
[2023-08-17 13:02:05,192][131794] Waiting for process rollout_proc5 to join...
[2023-08-17 13:02:05,193][131794] Waiting for process rollout_proc6 to join...
[2023-08-17 13:02:05,193][131794] Waiting for process rollout_proc7 to join...
[2023-08-17 13:02:05,194][131794] Batcher 0 profile tree view:
batching: 12.6388, releasing_batches: 0.0155
[2023-08-17 13:02:05,194][131794] InferenceWorker_p0-w0 profile tree view:
wait_policy: 0.0000
wait_policy_total: 2.6989
update_model: 2.1930
weight_update: 0.0007
one_step: 0.0019
handle_policy_step: 134.6435
deserialize: 6.0242, stack: 0.6403, obs_to_device_normalize: 30.9018, forward: 66.9499, send_messages: 8.4976
prepare_outputs: 15.8515
to_cpu: 10.1908
[2023-08-17 13:02:05,195][131794] Learner 0 profile tree view:
misc: 0.0062, prepare_batch: 6.1025
train: 17.4443
epoch_init: 0.0048, minibatch_init: 0.0045, losses_postprocess: 0.4413, kl_divergence: 0.3416, after_optimizer: 0.4372
calculate_losses: 6.4031
losses_init: 0.0025, forward_head: 0.4213, bptt_initial: 3.8098, tail: 0.4256, advantages_returns: 0.1042, losses: 0.8304
bptt: 0.6899
bptt_forward_core: 0.6553
update: 9.5344
clip: 4.9986
[2023-08-17 13:02:05,195][131794] RolloutWorker_w0 profile tree view:
wait_for_trajectories: 0.1145, enqueue_policy_requests: 4.7133, env_step: 65.9256, overhead: 6.2646, complete_rollouts: 0.1526
save_policy_outputs: 6.3662
split_output_tensors: 2.9670
[2023-08-17 13:02:05,195][131794] RolloutWorker_w7 profile tree view:
wait_for_trajectories: 0.1125, enqueue_policy_requests: 4.8606, env_step: 68.5610, overhead: 6.5985, complete_rollouts: 0.1617
save_policy_outputs: 6.9442
split_output_tensors: 3.1602
[2023-08-17 13:02:05,196][131794] Loop Runner_EvtLoop terminating...
[2023-08-17 13:02:05,197][131794] Runner profile tree view:
main_loop: 149.2631
[2023-08-17 13:02:05,197][131794] Collected {0: 10006528}, FPS: 40201.8
[2023-08-17 13:02:20,239][131794] Loading existing experiment configuration from /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/config.json
[2023-08-17 13:02:20,239][131794] Overriding arg 'num_workers' with value 1 passed from command line
[2023-08-17 13:02:20,240][131794] Adding new argument 'no_render'=True that is not in the saved config file!
[2023-08-17 13:02:20,240][131794] Adding new argument 'save_video'=True that is not in the saved config file!
[2023-08-17 13:02:20,241][131794] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
[2023-08-17 13:02:20,241][131794] Adding new argument 'video_name'=None that is not in the saved config file!
[2023-08-17 13:02:20,242][131794] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file!
[2023-08-17 13:02:20,243][131794] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
[2023-08-17 13:02:20,243][131794] Adding new argument 'push_to_hub'=False that is not in the saved config file!
[2023-08-17 13:02:20,244][131794] Adding new argument 'hf_repository'=None that is not in the saved config file!
[2023-08-17 13:02:20,244][131794] Adding new argument 'policy_index'=0 that is not in the saved config file!
[2023-08-17 13:02:20,245][131794] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
[2023-08-17 13:02:20,246][131794] Adding new argument 'train_script'=None that is not in the saved config file!
[2023-08-17 13:02:20,246][131794] Adding new argument 'enjoy_script'=None that is not in the saved config file!
[2023-08-17 13:02:20,247][131794] Using frameskip 1 and render_action_repeat=4 for evaluation
[2023-08-17 13:02:20,250][131794] RunningMeanStd input shape: (3, 72, 128)
[2023-08-17 13:02:20,251][131794] RunningMeanStd input shape: (1,)
[2023-08-17 13:02:20,257][131794] ConvEncoder: input_channels=3
[2023-08-17 13:02:20,280][131794] Conv encoder output size: 512
[2023-08-17 13:02:20,281][131794] Policy head output size: 512
[2023-08-17 13:02:20,723][131794] Loading state from checkpoint /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000002443_10006528.pth...
[2023-08-17 13:02:21,021][131794] Num frames 100...
[2023-08-17 13:02:21,076][131794] Num frames 200...
[2023-08-17 13:02:21,129][131794] Num frames 300...
[2023-08-17 13:02:21,183][131794] Num frames 400...
[2023-08-17 13:02:21,237][131794] Num frames 500...
[2023-08-17 13:02:21,314][131794] Avg episode rewards: #0: 8.440, true rewards: #0: 5.440
[2023-08-17 13:02:21,315][131794] Avg episode reward: 8.440, avg true_objective: 5.440
[2023-08-17 13:02:21,348][131794] Num frames 600...
[2023-08-17 13:02:21,402][131794] Num frames 700...
[2023-08-17 13:02:21,457][131794] Num frames 800...
[2023-08-17 13:02:21,512][131794] Num frames 900...
[2023-08-17 13:02:21,567][131794] Num frames 1000...
[2023-08-17 13:02:21,623][131794] Num frames 1100...
[2023-08-17 13:02:21,679][131794] Num frames 1200...
[2023-08-17 13:02:21,736][131794] Num frames 1300...
[2023-08-17 13:02:21,793][131794] Num frames 1400...
[2023-08-17 13:02:21,852][131794] Num frames 1500...
[2023-08-17 13:02:21,912][131794] Num frames 1600...
[2023-08-17 13:02:21,969][131794] Num frames 1700...
[2023-08-17 13:02:22,026][131794] Num frames 1800...
[2023-08-17 13:02:22,081][131794] Num frames 1900...
[2023-08-17 13:02:22,137][131794] Num frames 2000...
[2023-08-17 13:02:22,193][131794] Num frames 2100...
[2023-08-17 13:02:22,251][131794] Num frames 2200...
[2023-08-17 13:02:22,308][131794] Num frames 2300...
[2023-08-17 13:02:22,367][131794] Num frames 2400...
[2023-08-17 13:02:22,424][131794] Num frames 2500...
[2023-08-17 13:02:22,482][131794] Num frames 2600...
[2023-08-17 13:02:22,560][131794] Avg episode rewards: #0: 31.219, true rewards: #0: 13.220
[2023-08-17 13:02:22,560][131794] Avg episode reward: 31.219, avg true_objective: 13.220
[2023-08-17 13:02:22,593][131794] Num frames 2700...
[2023-08-17 13:02:22,649][131794] Num frames 2800...
[2023-08-17 13:02:22,705][131794] Num frames 2900...
[2023-08-17 13:02:22,760][131794] Num frames 3000...
[2023-08-17 13:02:22,817][131794] Num frames 3100...
[2023-08-17 13:02:22,874][131794] Num frames 3200...
[2023-08-17 13:02:22,931][131794] Num frames 3300...
[2023-08-17 13:02:22,986][131794] Num frames 3400...
[2023-08-17 13:02:23,040][131794] Num frames 3500...
[2023-08-17 13:02:23,095][131794] Num frames 3600...
[2023-08-17 13:02:23,150][131794] Num frames 3700...
[2023-08-17 13:02:23,204][131794] Num frames 3800...
[2023-08-17 13:02:23,310][131794] Avg episode rewards: #0: 30.323, true rewards: #0: 12.990
[2023-08-17 13:02:23,311][131794] Avg episode reward: 30.323, avg true_objective: 12.990
[2023-08-17 13:02:23,313][131794] Num frames 3900...
[2023-08-17 13:02:23,369][131794] Num frames 4000...
[2023-08-17 13:02:23,426][131794] Num frames 4100...
[2023-08-17 13:02:23,484][131794] Num frames 4200...
[2023-08-17 13:02:23,539][131794] Num frames 4300...
[2023-08-17 13:02:23,595][131794] Num frames 4400...
[2023-08-17 13:02:23,650][131794] Num frames 4500...
[2023-08-17 13:02:23,706][131794] Num frames 4600...
[2023-08-17 13:02:23,761][131794] Num frames 4700...
[2023-08-17 13:02:23,816][131794] Num frames 4800...
[2023-08-17 13:02:23,872][131794] Num frames 4900...
[2023-08-17 13:02:23,929][131794] Num frames 5000...
[2023-08-17 13:02:23,984][131794] Num frames 5100...
[2023-08-17 13:02:24,039][131794] Num frames 5200...
[2023-08-17 13:02:24,096][131794] Num frames 5300...
[2023-08-17 13:02:24,152][131794] Num frames 5400...
[2023-08-17 13:02:24,242][131794] Avg episode rewards: #0: 31.662, true rewards: #0: 13.662
[2023-08-17 13:02:24,243][131794] Avg episode reward: 31.662, avg true_objective: 13.662
[2023-08-17 13:02:24,263][131794] Num frames 5500...
[2023-08-17 13:02:24,318][131794] Num frames 5600...
[2023-08-17 13:02:24,374][131794] Num frames 5700...
[2023-08-17 13:02:24,428][131794] Num frames 5800...
[2023-08-17 13:02:24,483][131794] Num frames 5900...
[2023-08-17 13:02:24,538][131794] Num frames 6000...
[2023-08-17 13:02:24,631][131794] Avg episode rewards: #0: 27.146, true rewards: #0: 12.146
[2023-08-17 13:02:24,632][131794] Avg episode reward: 27.146, avg true_objective: 12.146
[2023-08-17 13:02:24,648][131794] Num frames 6100...
[2023-08-17 13:02:24,706][131794] Num frames 6200...
[2023-08-17 13:02:24,763][131794] Num frames 6300...
[2023-08-17 13:02:24,818][131794] Num frames 6400...
[2023-08-17 13:02:24,872][131794] Num frames 6500...
[2023-08-17 13:02:24,937][131794] Avg episode rewards: #0: 23.535, true rewards: #0: 10.868
[2023-08-17 13:02:24,938][131794] Avg episode reward: 23.535, avg true_objective: 10.868
[2023-08-17 13:02:24,985][131794] Num frames 6600...
[2023-08-17 13:02:25,040][131794] Num frames 6700...
[2023-08-17 13:02:25,094][131794] Num frames 6800...
[2023-08-17 13:02:25,150][131794] Num frames 6900...
[2023-08-17 13:02:25,205][131794] Num frames 7000...
[2023-08-17 13:02:25,260][131794] Num frames 7100...
[2023-08-17 13:02:25,315][131794] Num frames 7200...
[2023-08-17 13:02:25,416][131794] Avg episode rewards: #0: 21.984, true rewards: #0: 10.413
[2023-08-17 13:02:25,417][131794] Avg episode reward: 21.984, avg true_objective: 10.413
[2023-08-17 13:02:25,424][131794] Num frames 7300...
[2023-08-17 13:02:25,479][131794] Num frames 7400...
[2023-08-17 13:02:25,535][131794] Num frames 7500...
[2023-08-17 13:02:25,591][131794] Num frames 7600...
[2023-08-17 13:02:25,646][131794] Num frames 7700...
[2023-08-17 13:02:25,746][131794] Avg episode rewards: #0: 20.484, true rewards: #0: 9.734
[2023-08-17 13:02:25,747][131794] Avg episode reward: 20.484, avg true_objective: 9.734
[2023-08-17 13:02:25,755][131794] Num frames 7800...
[2023-08-17 13:02:25,809][131794] Num frames 7900...
[2023-08-17 13:02:25,864][131794] Num frames 8000...
[2023-08-17 13:02:25,918][131794] Num frames 8100...
[2023-08-17 13:02:25,972][131794] Num frames 8200...
[2023-08-17 13:02:26,027][131794] Num frames 8300...
[2023-08-17 13:02:26,082][131794] Num frames 8400...
[2023-08-17 13:02:26,167][131794] Avg episode rewards: #0: 19.510, true rewards: #0: 9.399
[2023-08-17 13:02:26,168][131794] Avg episode reward: 19.510, avg true_objective: 9.399
[2023-08-17 13:02:26,190][131794] Num frames 8500...
[2023-08-17 13:02:26,245][131794] Num frames 8600...
[2023-08-17 13:02:26,300][131794] Num frames 8700...
[2023-08-17 13:02:26,355][131794] Num frames 8800...
[2023-08-17 13:02:26,410][131794] Num frames 8900...
[2023-08-17 13:02:26,466][131794] Avg episode rewards: #0: 18.107, true rewards: #0: 8.907
[2023-08-17 13:02:26,467][131794] Avg episode reward: 18.107, avg true_objective: 8.907
[2023-08-17 13:02:34,995][131794] Replay video saved to /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/replay.mp4!
[2023-08-17 13:03:03,228][131794] Loading existing experiment configuration from /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/config.json
[2023-08-17 13:03:03,228][131794] Overriding arg 'num_workers' with value 1 passed from command line
[2023-08-17 13:03:03,229][131794] Adding new argument 'no_render'=True that is not in the saved config file!
[2023-08-17 13:03:03,229][131794] Adding new argument 'save_video'=True that is not in the saved config file!
[2023-08-17 13:03:03,230][131794] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
[2023-08-17 13:03:03,230][131794] Adding new argument 'video_name'=None that is not in the saved config file!
[2023-08-17 13:03:03,230][131794] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!
[2023-08-17 13:03:03,231][131794] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
[2023-08-17 13:03:03,231][131794] Adding new argument 'push_to_hub'=True that is not in the saved config file!
[2023-08-17 13:03:03,231][131794] Adding new argument 'hf_repository'='patonw/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file!
[2023-08-17 13:03:03,232][131794] Adding new argument 'policy_index'=0 that is not in the saved config file!
[2023-08-17 13:03:03,232][131794] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
[2023-08-17 13:03:03,233][131794] Adding new argument 'train_script'=None that is not in the saved config file!
[2023-08-17 13:03:03,233][131794] Adding new argument 'enjoy_script'=None that is not in the saved config file!
[2023-08-17 13:03:03,233][131794] Using frameskip 1 and render_action_repeat=4 for evaluation
[2023-08-17 13:03:03,238][131794] RunningMeanStd input shape: (3, 72, 128)
[2023-08-17 13:03:03,239][131794] RunningMeanStd input shape: (1,)
[2023-08-17 13:03:03,245][131794] ConvEncoder: input_channels=3
[2023-08-17 13:03:03,266][131794] Conv encoder output size: 512
[2023-08-17 13:03:03,267][131794] Policy head output size: 512
[2023-08-17 13:03:03,283][131794] Loading state from checkpoint /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000002443_10006528.pth...
[2023-08-17 13:03:03,601][131794] Num frames 100...
[2023-08-17 13:03:03,655][131794] Num frames 200...
[2023-08-17 13:03:03,719][131794] Num frames 300...
[2023-08-17 13:03:03,778][131794] Num frames 400...
[2023-08-17 13:03:03,835][131794] Num frames 500...
[2023-08-17 13:03:03,888][131794] Num frames 600...
[2023-08-17 13:03:03,942][131794] Num frames 700...
[2023-08-17 13:03:03,995][131794] Num frames 800...
[2023-08-17 13:03:04,049][131794] Num frames 900...
[2023-08-17 13:03:04,102][131794] Num frames 1000...
[2023-08-17 13:03:04,156][131794] Num frames 1100...
[2023-08-17 13:03:04,210][131794] Num frames 1200...
[2023-08-17 13:03:04,265][131794] Num frames 1300...
[2023-08-17 13:03:04,319][131794] Num frames 1400...
[2023-08-17 13:03:04,376][131794] Num frames 1500...
[2023-08-17 13:03:04,430][131794] Num frames 1600...
[2023-08-17 13:03:04,485][131794] Num frames 1700...
[2023-08-17 13:03:04,541][131794] Num frames 1800...
[2023-08-17 13:03:04,597][131794] Num frames 1900...
[2023-08-17 13:03:04,655][131794] Num frames 2000...
[2023-08-17 13:03:04,715][131794] Num frames 2100...
[2023-08-17 13:03:04,766][131794] Avg episode rewards: #0: 53.999, true rewards: #0: 21.000
[2023-08-17 13:03:04,767][131794] Avg episode reward: 53.999, avg true_objective: 21.000
[2023-08-17 13:03:04,825][131794] Num frames 2200...
[2023-08-17 13:03:04,883][131794] Num frames 2300...
[2023-08-17 13:03:04,942][131794] Num frames 2400...
[2023-08-17 13:03:04,999][131794] Num frames 2500...
[2023-08-17 13:03:05,056][131794] Num frames 2600...
[2023-08-17 13:03:05,114][131794] Num frames 2700...
[2023-08-17 13:03:05,173][131794] Num frames 2800...
[2023-08-17 13:03:05,231][131794] Num frames 2900...
[2023-08-17 13:03:05,291][131794] Num frames 3000...
[2023-08-17 13:03:05,348][131794] Num frames 3100...
[2023-08-17 13:03:05,408][131794] Num frames 3200...
[2023-08-17 13:03:05,466][131794] Num frames 3300...
[2023-08-17 13:03:05,526][131794] Num frames 3400...
[2023-08-17 13:03:05,586][131794] Num frames 3500...
[2023-08-17 13:03:05,672][131794] Avg episode rewards: #0: 44.770, true rewards: #0: 17.770
[2023-08-17 13:03:05,673][131794] Avg episode reward: 44.770, avg true_objective: 17.770
[2023-08-17 13:03:05,699][131794] Num frames 3600...
[2023-08-17 13:03:05,759][131794] Num frames 3700...
[2023-08-17 13:03:05,819][131794] Num frames 3800...
[2023-08-17 13:03:05,878][131794] Num frames 3900...
[2023-08-17 13:03:05,936][131794] Num frames 4000...
[2023-08-17 13:03:05,993][131794] Num frames 4100...
[2023-08-17 13:03:06,050][131794] Num frames 4200...
[2023-08-17 13:03:06,108][131794] Num frames 4300...
[2023-08-17 13:03:06,166][131794] Num frames 4400...
[2023-08-17 13:03:06,225][131794] Num frames 4500...
[2023-08-17 13:03:06,284][131794] Num frames 4600...
[2023-08-17 13:03:06,343][131794] Num frames 4700...
[2023-08-17 13:03:06,401][131794] Num frames 4800...
[2023-08-17 13:03:06,463][131794] Num frames 4900...
[2023-08-17 13:03:06,521][131794] Num frames 5000...
[2023-08-17 13:03:06,579][131794] Num frames 5100...
[2023-08-17 13:03:06,638][131794] Num frames 5200...
[2023-08-17 13:03:06,696][131794] Num frames 5300...
[2023-08-17 13:03:06,753][131794] Num frames 5400...
[2023-08-17 13:03:06,812][131794] Num frames 5500...
[2023-08-17 13:03:06,870][131794] Num frames 5600...
[2023-08-17 13:03:06,954][131794] Avg episode rewards: #0: 48.179, true rewards: #0: 18.847
[2023-08-17 13:03:06,955][131794] Avg episode reward: 48.179, avg true_objective: 18.847
[2023-08-17 13:03:06,981][131794] Num frames 5700...
[2023-08-17 13:03:07,037][131794] Num frames 5800...
[2023-08-17 13:03:07,094][131794] Num frames 5900...
[2023-08-17 13:03:07,152][131794] Num frames 6000...
[2023-08-17 13:03:07,210][131794] Num frames 6100...
[2023-08-17 13:03:07,269][131794] Num frames 6200...
[2023-08-17 13:03:07,327][131794] Num frames 6300...
[2023-08-17 13:03:07,385][131794] Num frames 6400...
[2023-08-17 13:03:07,443][131794] Num frames 6500...
[2023-08-17 13:03:07,500][131794] Num frames 6600...
[2023-08-17 13:03:07,559][131794] Num frames 6700...
[2023-08-17 13:03:07,617][131794] Num frames 6800...
[2023-08-17 13:03:07,679][131794] Avg episode rewards: #0: 42.537, true rewards: #0: 17.038
[2023-08-17 13:03:07,679][131794] Avg episode reward: 42.537, avg true_objective: 17.038
[2023-08-17 13:03:07,728][131794] Num frames 6900...
[2023-08-17 13:03:07,787][131794] Num frames 7000...
[2023-08-17 13:03:07,845][131794] Num frames 7100...
[2023-08-17 13:03:07,903][131794] Num frames 7200...
[2023-08-17 13:03:07,961][131794] Num frames 7300...
[2023-08-17 13:03:08,020][131794] Num frames 7400...
[2023-08-17 13:03:08,078][131794] Num frames 7500...
[2023-08-17 13:03:08,137][131794] Num frames 7600...
[2023-08-17 13:03:08,195][131794] Num frames 7700...
[2023-08-17 13:03:08,255][131794] Num frames 7800...
[2023-08-17 13:03:08,313][131794] Num frames 7900...
[2023-08-17 13:03:08,371][131794] Num frames 8000...
[2023-08-17 13:03:08,430][131794] Num frames 8100...
[2023-08-17 13:03:08,492][131794] Num frames 8200...
[2023-08-17 13:03:08,551][131794] Num frames 8300...
[2023-08-17 13:03:08,610][131794] Num frames 8400...
[2023-08-17 13:03:08,699][131794] Avg episode rewards: #0: 41.920, true rewards: #0: 16.920
[2023-08-17 13:03:08,699][131794] Avg episode reward: 41.920, avg true_objective: 16.920
[2023-08-17 13:03:08,724][131794] Num frames 8500...
[2023-08-17 13:03:08,783][131794] Num frames 8600...
[2023-08-17 13:03:08,842][131794] Num frames 8700...
[2023-08-17 13:03:08,901][131794] Num frames 8800...
[2023-08-17 13:03:08,960][131794] Num frames 8900...
[2023-08-17 13:03:09,019][131794] Num frames 9000...
[2023-08-17 13:03:09,074][131794] Avg episode rewards: #0: 36.173, true rewards: #0: 15.007
[2023-08-17 13:03:09,074][131794] Avg episode reward: 36.173, avg true_objective: 15.007
[2023-08-17 13:03:09,127][131794] Num frames 9100...
[2023-08-17 13:03:09,181][131794] Num frames 9200...
[2023-08-17 13:03:09,235][131794] Num frames 9300...
[2023-08-17 13:03:09,290][131794] Num frames 9400...
[2023-08-17 13:03:09,344][131794] Num frames 9500...
[2023-08-17 13:03:09,398][131794] Num frames 9600...
[2023-08-17 13:03:09,452][131794] Num frames 9700...
[2023-08-17 13:03:09,506][131794] Num frames 9800...
[2023-08-17 13:03:09,559][131794] Num frames 9900...
[2023-08-17 13:03:09,663][131794] Avg episode rewards: #0: 34.137, true rewards: #0: 14.280
[2023-08-17 13:03:09,664][131794] Avg episode reward: 34.137, avg true_objective: 14.280
[2023-08-17 13:03:09,667][131794] Num frames 10000...
[2023-08-17 13:03:09,720][131794] Num frames 10100...
[2023-08-17 13:03:09,775][131794] Num frames 10200...
[2023-08-17 13:03:09,831][131794] Num frames 10300...
[2023-08-17 13:03:09,887][131794] Num frames 10400...
[2023-08-17 13:03:09,942][131794] Num frames 10500...
[2023-08-17 13:03:09,997][131794] Num frames 10600...
[2023-08-17 13:03:10,101][131794] Avg episode rewards: #0: 32.115, true rewards: #0: 13.365
[2023-08-17 13:03:10,102][131794] Avg episode reward: 32.115, avg true_objective: 13.365
[2023-08-17 13:03:10,108][131794] Num frames 10700...
[2023-08-17 13:03:10,164][131794] Num frames 10800...
[2023-08-17 13:03:10,220][131794] Num frames 10900...
[2023-08-17 13:03:10,275][131794] Num frames 11000...
[2023-08-17 13:03:10,334][131794] Avg episode rewards: #0: 29.235, true rewards: #0: 12.236
[2023-08-17 13:03:10,335][131794] Avg episode reward: 29.235, avg true_objective: 12.236
[2023-08-17 13:03:10,387][131794] Num frames 11100...
[2023-08-17 13:03:10,442][131794] Num frames 11200...
[2023-08-17 13:03:10,497][131794] Num frames 11300...
[2023-08-17 13:03:10,553][131794] Num frames 11400...
[2023-08-17 13:03:10,610][131794] Num frames 11500...
[2023-08-17 13:03:10,666][131794] Num frames 11600...
[2023-08-17 13:03:10,720][131794] Num frames 11700...
[2023-08-17 13:03:10,779][131794] Num frames 11800...
[2023-08-17 13:03:10,862][131794] Avg episode rewards: #0: 28.046, true rewards: #0: 11.846
[2023-08-17 13:03:10,863][131794] Avg episode reward: 28.046, avg true_objective: 11.846
[2023-08-17 13:03:22,380][131794] Replay video saved to /home/patonw/code/learn/deep-rl-class/notebooks/unit8/train_dir/default_experiment/replay.mp4!
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