| [2023-02-25 13:36:50,795][00699] Saving configuration to /content/train_dir/default_experiment/config.json... | |
| [2023-02-25 13:36:50,798][00699] Rollout worker 0 uses device cpu | |
| [2023-02-25 13:36:50,799][00699] Rollout worker 1 uses device cpu | |
| [2023-02-25 13:36:50,803][00699] Rollout worker 2 uses device cpu | |
| [2023-02-25 13:36:50,804][00699] Rollout worker 3 uses device cpu | |
| [2023-02-25 13:36:50,806][00699] Rollout worker 4 uses device cpu | |
| [2023-02-25 13:36:50,807][00699] Rollout worker 5 uses device cpu | |
| [2023-02-25 13:36:50,809][00699] Rollout worker 6 uses device cpu | |
| [2023-02-25 13:36:50,810][00699] Rollout worker 7 uses device cpu | |
| [2023-02-25 13:36:51,020][00699] Using GPUs [0] for process 0 (actually maps to GPUs [0]) | |
| [2023-02-25 13:36:51,025][00699] InferenceWorker_p0-w0: min num requests: 2 | |
| [2023-02-25 13:36:51,055][00699] Starting all processes... | |
| [2023-02-25 13:36:51,057][00699] Starting process learner_proc0 | |
| [2023-02-25 13:36:51,111][00699] Starting all processes... | |
| [2023-02-25 13:36:51,129][00699] Starting process inference_proc0-0 | |
| [2023-02-25 13:36:51,133][00699] Starting process rollout_proc0 | |
| [2023-02-25 13:36:51,133][00699] Starting process rollout_proc1 | |
| [2023-02-25 13:36:51,141][00699] Starting process rollout_proc3 | |
| [2023-02-25 13:36:51,141][00699] Starting process rollout_proc4 | |
| [2023-02-25 13:36:51,141][00699] Starting process rollout_proc5 | |
| [2023-02-25 13:36:51,141][00699] Starting process rollout_proc6 | |
| [2023-02-25 13:36:51,141][00699] Starting process rollout_proc7 | |
| [2023-02-25 13:36:51,141][00699] Starting process rollout_proc2 | |
| [2023-02-25 13:37:01,955][10893] Using GPUs [0] for process 0 (actually maps to GPUs [0]) | |
| [2023-02-25 13:37:01,963][10893] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0 | |
| [2023-02-25 13:37:02,647][10911] Worker 5 uses CPU cores [1] | |
| [2023-02-25 13:37:03,151][10907] Using GPUs [0] for process 0 (actually maps to GPUs [0]) | |
| [2023-02-25 13:37:03,160][10907] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0 | |
| [2023-02-25 13:37:03,356][10909] Worker 1 uses CPU cores [1] | |
| [2023-02-25 13:37:03,421][10908] Worker 0 uses CPU cores [0] | |
| [2023-02-25 13:37:03,684][10912] Worker 4 uses CPU cores [0] | |
| [2023-02-25 13:37:03,765][10910] Worker 3 uses CPU cores [1] | |
| [2023-02-25 13:37:03,771][10914] Worker 7 uses CPU cores [1] | |
| [2023-02-25 13:37:03,932][10915] Worker 2 uses CPU cores [0] | |
| [2023-02-25 13:37:03,933][10913] Worker 6 uses CPU cores [0] | |
| [2023-02-25 13:37:04,054][10907] Num visible devices: 1 | |
| [2023-02-25 13:37:04,057][10893] Num visible devices: 1 | |
| [2023-02-25 13:37:04,078][10893] Starting seed is not provided | |
| [2023-02-25 13:37:04,079][10893] Using GPUs [0] for process 0 (actually maps to GPUs [0]) | |
| [2023-02-25 13:37:04,079][10893] Initializing actor-critic model on device cuda:0 | |
| [2023-02-25 13:37:04,079][10893] RunningMeanStd input shape: (3, 72, 128) | |
| [2023-02-25 13:37:04,081][10893] RunningMeanStd input shape: (1,) | |
| [2023-02-25 13:37:04,135][10893] ConvEncoder: input_channels=3 | |
| [2023-02-25 13:37:04,617][10893] Conv encoder output size: 512 | |
| [2023-02-25 13:37:04,618][10893] Policy head output size: 512 | |
| [2023-02-25 13:37:04,697][10893] Created Actor Critic model with architecture: | |
| [2023-02-25 13:37:04,697][10893] 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) | |
| ) | |
| ) | |
| [2023-02-25 13:37:11,013][00699] Heartbeat connected on Batcher_0 | |
| [2023-02-25 13:37:11,021][00699] Heartbeat connected on InferenceWorker_p0-w0 | |
| [2023-02-25 13:37:11,031][00699] Heartbeat connected on RolloutWorker_w0 | |
| [2023-02-25 13:37:11,035][00699] Heartbeat connected on RolloutWorker_w1 | |
| [2023-02-25 13:37:11,038][00699] Heartbeat connected on RolloutWorker_w2 | |
| [2023-02-25 13:37:11,041][00699] Heartbeat connected on RolloutWorker_w3 | |
| [2023-02-25 13:37:11,045][00699] Heartbeat connected on RolloutWorker_w4 | |
| [2023-02-25 13:37:11,050][00699] Heartbeat connected on RolloutWorker_w6 | |
| [2023-02-25 13:37:11,051][00699] Heartbeat connected on RolloutWorker_w5 | |
| [2023-02-25 13:37:11,054][00699] Heartbeat connected on RolloutWorker_w7 | |
| [2023-02-25 13:37:13,611][10893] Using optimizer <class 'torch.optim.adam.Adam'> | |
| [2023-02-25 13:37:13,612][10893] No checkpoints found | |
| [2023-02-25 13:37:13,612][10893] Did not load from checkpoint, starting from scratch! | |
| [2023-02-25 13:37:13,613][10893] Initialized policy 0 weights for model version 0 | |
| [2023-02-25 13:37:13,617][10893] Using GPUs [0] for process 0 (actually maps to GPUs [0]) | |
| [2023-02-25 13:37:13,624][10893] LearnerWorker_p0 finished initialization! | |
| [2023-02-25 13:37:13,625][00699] Heartbeat connected on LearnerWorker_p0 | |
| [2023-02-25 13:37:13,719][10907] RunningMeanStd input shape: (3, 72, 128) | |
| [2023-02-25 13:37:13,721][10907] RunningMeanStd input shape: (1,) | |
| [2023-02-25 13:37:13,739][10907] ConvEncoder: input_channels=3 | |
| [2023-02-25 13:37:13,836][10907] Conv encoder output size: 512 | |
| [2023-02-25 13:37:13,836][10907] Policy head output size: 512 | |
| [2023-02-25 13:37:15,425][00699] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 0. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) | |
| [2023-02-25 13:37:16,389][00699] Inference worker 0-0 is ready! | |
| [2023-02-25 13:37:16,391][00699] All inference workers are ready! Signal rollout workers to start! | |
| [2023-02-25 13:37:16,499][10913] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:37:16,511][10915] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:37:16,515][10912] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:37:16,556][00699] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 699], exiting... | |
| [2023-02-25 13:37:16,562][10893] Stopping Batcher_0... | |
| [2023-02-25 13:37:16,563][10893] Loop batcher_evt_loop terminating... | |
| [2023-02-25 13:37:16,561][00699] Runner profile tree view: | |
| main_loop: 25.5057 | |
| [2023-02-25 13:37:16,568][10893] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000000_0.pth... | |
| [2023-02-25 13:37:16,566][00699] Collected {0: 0}, FPS: 0.0 | |
| [2023-02-25 13:37:16,555][10908] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:37:16,627][10907] Weights refcount: 2 0 | |
| [2023-02-25 13:37:16,639][10907] Stopping InferenceWorker_p0-w0... | |
| [2023-02-25 13:37:16,643][10907] Loop inference_proc0-0_evt_loop terminating... | |
| [2023-02-25 13:37:16,653][00699] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json | |
| [2023-02-25 13:37:16,666][10893] Stopping LearnerWorker_p0... | |
| [2023-02-25 13:37:16,666][10893] Loop learner_proc0_evt_loop terminating... | |
| [2023-02-25 13:37:16,661][00699] Overriding arg 'num_workers' with value 1 passed from command line | |
| [2023-02-25 13:37:16,667][00699] Adding new argument 'no_render'=True that is not in the saved config file! | |
| [2023-02-25 13:37:16,672][00699] Adding new argument 'save_video'=True that is not in the saved config file! | |
| [2023-02-25 13:37:16,678][00699] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! | |
| [2023-02-25 13:37:16,683][00699] Adding new argument 'video_name'=None that is not in the saved config file! | |
| [2023-02-25 13:37:16,689][00699] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file! | |
| [2023-02-25 13:37:16,690][00699] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! | |
| [2023-02-25 13:37:16,693][00699] Adding new argument 'push_to_hub'=False that is not in the saved config file! | |
| [2023-02-25 13:37:16,695][00699] Adding new argument 'hf_repository'=None that is not in the saved config file! | |
| [2023-02-25 13:37:16,700][00699] Adding new argument 'policy_index'=0 that is not in the saved config file! | |
| [2023-02-25 13:37:16,706][00699] Adding new argument 'eval_deterministic'=False that is not in the saved config file! | |
| [2023-02-25 13:37:16,711][00699] Adding new argument 'train_script'=None that is not in the saved config file! | |
| [2023-02-25 13:37:16,713][00699] Adding new argument 'enjoy_script'=None that is not in the saved config file! | |
| [2023-02-25 13:37:16,719][00699] Using frameskip 1 and render_action_repeat=4 for evaluation | |
| [2023-02-25 13:37:16,786][00699] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:37:16,795][00699] RunningMeanStd input shape: (3, 72, 128) | |
| [2023-02-25 13:37:16,803][00699] RunningMeanStd input shape: (1,) | |
| [2023-02-25 13:37:16,866][10911] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:37:16,862][00699] ConvEncoder: input_channels=3 | |
| [2023-02-25 13:37:16,906][10914] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:37:16,918][10910] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:37:17,007][10909] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:37:17,199][00699] Conv encoder output size: 512 | |
| [2023-02-25 13:37:17,214][00699] Policy head output size: 512 | |
| [2023-02-25 13:37:20,333][10915] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:37:20,335][10913] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:37:20,337][10908] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:37:20,515][10911] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:37:22,092][10914] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:37:22,349][00699] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000000_0.pth... | |
| [2023-02-25 13:37:22,486][10912] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:37:22,488][10913] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:37:22,490][10915] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:37:22,495][10908] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:37:22,798][10914] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:37:22,874][00699] VizDoom game.init() threw an exception SignalException('Signal SIGINT received. ViZDoom instance has been closed.'). Terminate process... | |
| [2023-02-25 13:37:22,881][10915] VizDoom game.init() threw an exception SignalException('Signal SIGINT received. ViZDoom instance has been closed.'). Terminate process... | |
| [2023-02-25 13:37:22,889][10908] VizDoom game.init() threw an exception SignalException('Signal SIGINT received. ViZDoom instance has been closed.'). Terminate process... | |
| [2023-02-25 13:37:22,892][10912] VizDoom game.init() threw an exception SignalException('Signal SIGINT received. ViZDoom instance has been closed.'). Terminate process... | |
| [2023-02-25 13:37:22,897][10909] VizDoom game.init() threw an exception SignalException('Signal SIGINT received. ViZDoom instance has been closed.'). Terminate process... | |
| [2023-02-25 13:37:22,889][10914] EvtLoop [rollout_proc7_evt_loop, process=rollout_proc7] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Sampler', signal_name='_inference_workers_initialized'), args=() | |
| Traceback (most recent call last): | |
| File "/usr/local/lib/python3.8/dist-packages/signal_slot/signal_slot.py", line 355, in _process_signal | |
| slot_callable(*args) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/rollout_worker.py", line 150, in init | |
| env_runner.init(self.timing) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 418, in init | |
| self._reset() | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 439, in _reset | |
| observations, rew, terminated, truncated, info = e.step(actions) | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 319, in step | |
| return self.env.step(action) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 129, in step | |
| obs, rew, terminated, truncated, info = self.env.step(action) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 115, in step | |
| obs, rew, terminated, truncated, info = self.env.step(action) | |
| File "/usr/local/lib/python3.8/dist-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 "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 384, in step | |
| observation, reward, terminated, truncated, info = self.env.step(action) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/envs/env_wrappers.py", line 88, in step | |
| obs, reward, terminated, truncated, info = self.env.step(action) | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 319, in step | |
| return self.env.step(action) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 54, in step | |
| obs, reward, terminated, truncated, info = self.env.step(action) | |
| File "/usr/local/lib/python3.8/dist-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. | |
| [2023-02-25 13:37:22,914][10914] Unhandled exception Signal SIGINT received. ViZDoom instance has been closed. in evt loop rollout_proc7_evt_loop | |
| [2023-02-25 13:37:22,902][10912] EvtLoop [rollout_proc4_evt_loop, process=rollout_proc4] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Sampler', signal_name='_inference_workers_initialized'), args=() | |
| Traceback (most recent call last): | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 228, in _game_init | |
| self.game.init() | |
| vizdoom.vizdoom.SignalException: Signal SIGINT received. ViZDoom instance has been closed. | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/usr/local/lib/python3.8/dist-packages/signal_slot/signal_slot.py", line 355, in _process_signal | |
| slot_callable(*args) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/rollout_worker.py", line 150, in init | |
| env_runner.init(self.timing) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 418, in init | |
| self._reset() | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 430, in _reset | |
| observations, info = e.reset(seed=seed) # new way of doing seeding since Gym 0.26.0 | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 125, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 110, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 30, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 379, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/envs/env_wrappers.py", line 84, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 51, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 323, in reset | |
| self._ensure_initialized() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 274, in _ensure_initialized | |
| self.initialize() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 269, in initialize | |
| self._game_init() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 244, in _game_init | |
| raise EnvCriticalError() | |
| sample_factory.envs.env_utils.EnvCriticalError | |
| [2023-02-25 13:37:22,915][10912] Unhandled exception in evt loop rollout_proc4_evt_loop | |
| [2023-02-25 13:37:22,902][10909] EvtLoop [rollout_proc1_evt_loop, process=rollout_proc1] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Sampler', signal_name='_inference_workers_initialized'), args=() | |
| Traceback (most recent call last): | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 228, in _game_init | |
| self.game.init() | |
| vizdoom.vizdoom.SignalException: Signal SIGINT received. ViZDoom instance has been closed. | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/usr/local/lib/python3.8/dist-packages/signal_slot/signal_slot.py", line 355, in _process_signal | |
| slot_callable(*args) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/rollout_worker.py", line 150, in init | |
| env_runner.init(self.timing) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 418, in init | |
| self._reset() | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 430, in _reset | |
| observations, info = e.reset(seed=seed) # new way of doing seeding since Gym 0.26.0 | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 125, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 110, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 30, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 379, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/envs/env_wrappers.py", line 84, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 51, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 323, in reset | |
| self._ensure_initialized() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 274, in _ensure_initialized | |
| self.initialize() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 269, in initialize | |
| self._game_init() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 244, in _game_init | |
| raise EnvCriticalError() | |
| sample_factory.envs.env_utils.EnvCriticalError | |
| [2023-02-25 13:37:22,930][10909] Unhandled exception in evt loop rollout_proc1_evt_loop | |
| [2023-02-25 13:37:22,882][10915] 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 "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 228, in _game_init | |
| self.game.init() | |
| vizdoom.vizdoom.SignalException: Signal SIGINT received. ViZDoom instance has been closed. | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/usr/local/lib/python3.8/dist-packages/signal_slot/signal_slot.py", line 355, in _process_signal | |
| slot_callable(*args) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/rollout_worker.py", line 150, in init | |
| env_runner.init(self.timing) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 418, in init | |
| self._reset() | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 430, in _reset | |
| observations, info = e.reset(seed=seed) # new way of doing seeding since Gym 0.26.0 | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 125, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 110, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 30, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 379, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/envs/env_wrappers.py", line 84, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 51, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 323, in reset | |
| self._ensure_initialized() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 274, in _ensure_initialized | |
| self.initialize() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 269, in initialize | |
| self._game_init() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 244, in _game_init | |
| raise EnvCriticalError() | |
| sample_factory.envs.env_utils.EnvCriticalError | |
| [2023-02-25 13:37:22,931][10915] Unhandled exception in evt loop rollout_proc2_evt_loop | |
| [2023-02-25 13:37:22,893][10908] EvtLoop [rollout_proc0_evt_loop, process=rollout_proc0] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Sampler', signal_name='_inference_workers_initialized'), args=() | |
| Traceback (most recent call last): | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 228, in _game_init | |
| self.game.init() | |
| vizdoom.vizdoom.SignalException: Signal SIGINT received. ViZDoom instance has been closed. | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/usr/local/lib/python3.8/dist-packages/signal_slot/signal_slot.py", line 355, in _process_signal | |
| slot_callable(*args) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/rollout_worker.py", line 150, in init | |
| env_runner.init(self.timing) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 418, in init | |
| self._reset() | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 430, in _reset | |
| observations, info = e.reset(seed=seed) # new way of doing seeding since Gym 0.26.0 | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 125, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 110, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 30, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 379, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sample_factory/envs/env_wrappers.py", line 84, in reset | |
| obs, info = self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 51, in reset | |
| return self.env.reset(**kwargs) | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 323, in reset | |
| self._ensure_initialized() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 274, in _ensure_initialized | |
| self.initialize() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 269, in initialize | |
| self._game_init() | |
| File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 244, in _game_init | |
| raise EnvCriticalError() | |
| sample_factory.envs.env_utils.EnvCriticalError | |
| [2023-02-25 13:37:22,935][10908] Unhandled exception in evt loop rollout_proc0_evt_loop | |
| [2023-02-25 13:37:24,225][10910] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:37:24,579][10913] Decorrelating experience for 64 frames... | |
| [2023-02-25 13:37:24,610][10910] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:37:25,165][10911] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:37:25,263][10910] Decorrelating experience for 64 frames... | |
| [2023-02-25 13:37:25,515][10913] Decorrelating experience for 96 frames... | |
| [2023-02-25 13:37:25,597][10913] Stopping RolloutWorker_w6... | |
| [2023-02-25 13:37:25,598][10913] Loop rollout_proc6_evt_loop terminating... | |
| [2023-02-25 13:37:25,929][10911] Decorrelating experience for 64 frames... | |
| [2023-02-25 13:37:25,966][10910] Decorrelating experience for 96 frames... | |
| [2023-02-25 13:37:26,051][10910] Stopping RolloutWorker_w3... | |
| [2023-02-25 13:37:26,052][10910] Loop rollout_proc3_evt_loop terminating... | |
| [2023-02-25 13:37:26,458][10911] Decorrelating experience for 96 frames... | |
| [2023-02-25 13:37:26,524][10911] Stopping RolloutWorker_w5... | |
| [2023-02-25 13:37:26,524][10911] Loop rollout_proc5_evt_loop terminating... | |
| [2023-02-25 13:37:49,127][00699] Environment doom_basic already registered, overwriting... | |
| [2023-02-25 13:37:49,129][00699] Environment doom_two_colors_easy already registered, overwriting... | |
| [2023-02-25 13:37:49,131][00699] Environment doom_two_colors_hard already registered, overwriting... | |
| [2023-02-25 13:37:49,135][00699] Environment doom_dm already registered, overwriting... | |
| [2023-02-25 13:37:49,138][00699] Environment doom_dwango5 already registered, overwriting... | |
| [2023-02-25 13:37:49,139][00699] Environment doom_my_way_home_flat_actions already registered, overwriting... | |
| [2023-02-25 13:37:49,140][00699] Environment doom_defend_the_center_flat_actions already registered, overwriting... | |
| [2023-02-25 13:37:49,141][00699] Environment doom_my_way_home already registered, overwriting... | |
| [2023-02-25 13:37:49,142][00699] Environment doom_deadly_corridor already registered, overwriting... | |
| [2023-02-25 13:37:49,143][00699] Environment doom_defend_the_center already registered, overwriting... | |
| [2023-02-25 13:37:49,145][00699] Environment doom_defend_the_line already registered, overwriting... | |
| [2023-02-25 13:37:49,146][00699] Environment doom_health_gathering already registered, overwriting... | |
| [2023-02-25 13:37:49,147][00699] Environment doom_health_gathering_supreme already registered, overwriting... | |
| [2023-02-25 13:37:49,148][00699] Environment doom_battle already registered, overwriting... | |
| [2023-02-25 13:37:49,149][00699] Environment doom_battle2 already registered, overwriting... | |
| [2023-02-25 13:37:49,150][00699] Environment doom_duel_bots already registered, overwriting... | |
| [2023-02-25 13:37:49,151][00699] Environment doom_deathmatch_bots already registered, overwriting... | |
| [2023-02-25 13:37:49,153][00699] Environment doom_duel already registered, overwriting... | |
| [2023-02-25 13:37:49,154][00699] Environment doom_deathmatch_full already registered, overwriting... | |
| [2023-02-25 13:37:49,155][00699] Environment doom_benchmark already registered, overwriting... | |
| [2023-02-25 13:37:49,156][00699] register_encoder_factory: <function make_vizdoom_encoder at 0x7f2bc8e5b430> | |
| [2023-02-25 13:37:49,184][00699] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json | |
| [2023-02-25 13:37:49,190][00699] Experiment dir /content/train_dir/default_experiment already exists! | |
| [2023-02-25 13:37:49,192][00699] Resuming existing experiment from /content/train_dir/default_experiment... | |
| [2023-02-25 13:37:49,193][00699] Weights and Biases integration disabled | |
| [2023-02-25 13:37:49,196][00699] Environment var CUDA_VISIBLE_DEVICES is 0 | |
| [2023-02-25 13:37:50,634][00699] Starting experiment with the following configuration: | |
| help=False | |
| algo=APPO | |
| env=doom_health_gathering_supreme | |
| experiment=default_experiment | |
| train_dir=/content/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=4000000 | |
| 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=False | |
| 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=unknown | |
| git_repo_name=not a git repository | |
| [2023-02-25 13:37:50,636][00699] Saving configuration to /content/train_dir/default_experiment/config.json... | |
| [2023-02-25 13:37:50,643][00699] Rollout worker 0 uses device cpu | |
| [2023-02-25 13:37:50,644][00699] Rollout worker 1 uses device cpu | |
| [2023-02-25 13:37:50,647][00699] Rollout worker 2 uses device cpu | |
| [2023-02-25 13:37:50,649][00699] Rollout worker 3 uses device cpu | |
| [2023-02-25 13:37:50,653][00699] Rollout worker 4 uses device cpu | |
| [2023-02-25 13:37:50,658][00699] Rollout worker 5 uses device cpu | |
| [2023-02-25 13:37:50,659][00699] Rollout worker 6 uses device cpu | |
| [2023-02-25 13:37:50,663][00699] Rollout worker 7 uses device cpu | |
| [2023-02-25 13:37:50,791][00699] Using GPUs [0] for process 0 (actually maps to GPUs [0]) | |
| [2023-02-25 13:37:50,792][00699] InferenceWorker_p0-w0: min num requests: 2 | |
| [2023-02-25 13:37:50,826][00699] Starting all processes... | |
| [2023-02-25 13:37:50,827][00699] Starting process learner_proc0 | |
| [2023-02-25 13:37:50,924][00699] Starting all processes... | |
| [2023-02-25 13:37:50,934][00699] Starting process inference_proc0-0 | |
| [2023-02-25 13:37:50,934][00699] Starting process rollout_proc0 | |
| [2023-02-25 13:37:50,939][00699] Starting process rollout_proc1 | |
| [2023-02-25 13:37:50,939][00699] Starting process rollout_proc2 | |
| [2023-02-25 13:37:50,939][00699] Starting process rollout_proc3 | |
| [2023-02-25 13:37:50,939][00699] Starting process rollout_proc4 | |
| [2023-02-25 13:37:50,939][00699] Starting process rollout_proc5 | |
| [2023-02-25 13:37:50,939][00699] Starting process rollout_proc6 | |
| [2023-02-25 13:37:50,939][00699] Starting process rollout_proc7 | |
| [2023-02-25 13:38:02,761][12789] Using GPUs [0] for process 0 (actually maps to GPUs [0]) | |
| [2023-02-25 13:38:02,761][12789] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0 | |
| [2023-02-25 13:38:02,816][12789] Num visible devices: 1 | |
| [2023-02-25 13:38:02,863][12789] Starting seed is not provided | |
| [2023-02-25 13:38:02,864][12789] Using GPUs [0] for process 0 (actually maps to GPUs [0]) | |
| [2023-02-25 13:38:02,864][12789] Initializing actor-critic model on device cuda:0 | |
| [2023-02-25 13:38:02,865][12789] RunningMeanStd input shape: (3, 72, 128) | |
| [2023-02-25 13:38:02,866][12789] RunningMeanStd input shape: (1,) | |
| [2023-02-25 13:38:02,945][12789] ConvEncoder: input_channels=3 | |
| [2023-02-25 13:38:03,118][12808] Worker 2 uses CPU cores [0] | |
| [2023-02-25 13:38:03,401][12803] Using GPUs [0] for process 0 (actually maps to GPUs [0]) | |
| [2023-02-25 13:38:03,402][12803] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0 | |
| [2023-02-25 13:38:03,459][12803] Num visible devices: 1 | |
| [2023-02-25 13:38:03,661][12805] Worker 0 uses CPU cores [0] | |
| [2023-02-25 13:38:03,678][12789] Conv encoder output size: 512 | |
| [2023-02-25 13:38:03,678][12789] Policy head output size: 512 | |
| [2023-02-25 13:38:03,680][12804] Worker 1 uses CPU cores [1] | |
| [2023-02-25 13:38:03,782][12789] Created Actor Critic model with architecture: | |
| [2023-02-25 13:38:03,788][12789] 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) | |
| ) | |
| ) | |
| [2023-02-25 13:38:04,128][12809] Worker 4 uses CPU cores [0] | |
| [2023-02-25 13:38:04,180][12813] Worker 3 uses CPU cores [1] | |
| [2023-02-25 13:38:04,182][12822] Worker 7 uses CPU cores [1] | |
| [2023-02-25 13:38:04,211][12819] Worker 5 uses CPU cores [1] | |
| [2023-02-25 13:38:04,243][12814] Worker 6 uses CPU cores [0] | |
| [2023-02-25 13:38:06,384][12789] Using optimizer <class 'torch.optim.adam.Adam'> | |
| [2023-02-25 13:38:06,385][12789] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000000_0.pth... | |
| [2023-02-25 13:38:06,399][12789] Loading model from checkpoint | |
| [2023-02-25 13:38:06,400][12789] Loaded experiment state at self.train_step=0, self.env_steps=0 | |
| [2023-02-25 13:38:06,401][12789] Initialized policy 0 weights for model version 0 | |
| [2023-02-25 13:38:06,403][12789] Using GPUs [0] for process 0 (actually maps to GPUs [0]) | |
| [2023-02-25 13:38:06,414][12789] LearnerWorker_p0 finished initialization! | |
| [2023-02-25 13:38:06,528][12803] RunningMeanStd input shape: (3, 72, 128) | |
| [2023-02-25 13:38:06,530][12803] RunningMeanStd input shape: (1,) | |
| [2023-02-25 13:38:06,549][12803] ConvEncoder: input_channels=3 | |
| [2023-02-25 13:38:06,657][12803] Conv encoder output size: 512 | |
| [2023-02-25 13:38:06,657][12803] Policy head output size: 512 | |
| [2023-02-25 13:38:09,041][00699] Inference worker 0-0 is ready! | |
| [2023-02-25 13:38:09,043][00699] All inference workers are ready! Signal rollout workers to start! | |
| [2023-02-25 13:38:09,155][12819] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:38:09,169][12822] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:38:09,168][12804] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:38:09,166][12813] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:38:09,196][00699] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 0. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) | |
| [2023-02-25 13:38:09,211][12808] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:38:09,220][12814] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:38:09,223][12809] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:38:09,231][12805] Doom resolution: 160x120, resize resolution: (128, 72) | |
| [2023-02-25 13:38:09,665][12808] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:38:10,341][12808] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:38:10,368][12814] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:38:10,631][12819] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:38:10,641][12813] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:38:10,639][12804] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:38:10,646][12822] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:38:10,783][00699] Heartbeat connected on Batcher_0 | |
| [2023-02-25 13:38:10,789][00699] Heartbeat connected on LearnerWorker_p0 | |
| [2023-02-25 13:38:10,827][00699] Heartbeat connected on InferenceWorker_p0-w0 | |
| [2023-02-25 13:38:10,966][12808] Decorrelating experience for 64 frames... | |
| [2023-02-25 13:38:11,731][12819] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:38:11,747][12813] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:38:11,745][12822] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:38:11,774][12805] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:38:11,778][12809] Decorrelating experience for 0 frames... | |
| [2023-02-25 13:38:12,181][12808] Decorrelating experience for 96 frames... | |
| [2023-02-25 13:38:12,375][00699] Heartbeat connected on RolloutWorker_w2 | |
| [2023-02-25 13:38:12,915][12814] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:38:12,938][12805] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:38:13,102][12804] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:38:13,276][12809] Decorrelating experience for 32 frames... | |
| [2023-02-25 13:38:13,696][12805] Decorrelating experience for 64 frames... | |
| [2023-02-25 13:38:13,771][12813] Decorrelating experience for 64 frames... | |
| [2023-02-25 13:38:14,196][00699] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 0.0. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) | |
| [2023-02-25 13:38:14,266][12809] Decorrelating experience for 64 frames... | |
| [2023-02-25 13:38:14,973][12814] Decorrelating experience for 64 frames... | |
| [2023-02-25 13:38:15,029][12809] Decorrelating experience for 96 frames... | |
| [2023-02-25 13:38:15,172][00699] Heartbeat connected on RolloutWorker_w4 | |
| [2023-02-25 13:38:15,531][12822] Decorrelating experience for 64 frames... | |
| [2023-02-25 13:38:16,136][12804] Decorrelating experience for 64 frames... | |
| [2023-02-25 13:38:16,653][12814] Decorrelating experience for 96 frames... | |
| [2023-02-25 13:38:16,733][00699] Heartbeat connected on RolloutWorker_w6 | |
| [2023-02-25 13:38:17,407][12819] Decorrelating experience for 64 frames... | |
| [2023-02-25 13:38:17,940][12805] Decorrelating experience for 96 frames... | |
| [2023-02-25 13:38:18,032][00699] Heartbeat connected on RolloutWorker_w0 | |
| [2023-02-25 13:38:18,531][12822] Decorrelating experience for 96 frames... | |
| [2023-02-25 13:38:18,964][12813] Decorrelating experience for 96 frames... | |
| [2023-02-25 13:38:19,024][00699] Heartbeat connected on RolloutWorker_w7 | |
| [2023-02-25 13:38:19,142][12804] Decorrelating experience for 96 frames... | |
| [2023-02-25 13:38:19,196][00699] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 2.6. Samples: 26. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) | |
| [2023-02-25 13:38:19,318][00699] Heartbeat connected on RolloutWorker_w3 | |
| [2023-02-25 13:38:19,633][00699] Heartbeat connected on RolloutWorker_w1 | |
| [2023-02-25 13:38:20,921][12819] Decorrelating experience for 96 frames... | |
| [2023-02-25 13:38:21,595][00699] Heartbeat connected on RolloutWorker_w5 | |
| [2023-02-25 13:38:23,189][12789] Signal inference workers to stop experience collection... | |
| [2023-02-25 13:38:23,196][12803] InferenceWorker_p0-w0: stopping experience collection | |
| [2023-02-25 13:38:24,196][00699] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 177.5. Samples: 2662. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) | |
| [2023-02-25 13:38:24,202][00699] Avg episode reward: [(0, '2.199')] | |
| [2023-02-25 13:38:25,924][12789] Signal inference workers to resume experience collection... | |
| [2023-02-25 13:38:25,925][12803] InferenceWorker_p0-w0: resuming experience collection | |
| [2023-02-25 13:38:29,199][00699] Fps is (10 sec: 1638.0, 60 sec: 819.1, 300 sec: 819.1). Total num frames: 16384. Throughput: 0: 179.2. Samples: 3584. Policy #0 lag: (min: 0.0, avg: 1.1, max: 2.0) | |
| [2023-02-25 13:38:29,203][00699] Avg episode reward: [(0, '3.435')] | |
| [2023-02-25 13:38:34,197][00699] Fps is (10 sec: 3276.7, 60 sec: 1310.7, 300 sec: 1310.7). Total num frames: 32768. Throughput: 0: 362.8. Samples: 9070. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:38:34,204][00699] Avg episode reward: [(0, '3.939')] | |
| [2023-02-25 13:38:35,652][12803] Updated weights for policy 0, policy_version 10 (0.0018) | |
| [2023-02-25 13:38:39,200][00699] Fps is (10 sec: 3276.2, 60 sec: 1638.2, 300 sec: 1638.2). Total num frames: 49152. Throughput: 0: 445.7. Samples: 13374. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:38:39,211][00699] Avg episode reward: [(0, '4.401')] | |
| [2023-02-25 13:38:44,196][00699] Fps is (10 sec: 3276.9, 60 sec: 1872.5, 300 sec: 1872.5). Total num frames: 65536. Throughput: 0: 443.0. Samples: 15504. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:38:44,200][00699] Avg episode reward: [(0, '4.297')] | |
| [2023-02-25 13:38:47,260][12803] Updated weights for policy 0, policy_version 20 (0.0013) | |
| [2023-02-25 13:38:49,196][00699] Fps is (10 sec: 4097.6, 60 sec: 2252.8, 300 sec: 2252.8). Total num frames: 90112. Throughput: 0: 551.7. Samples: 22066. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:38:49,204][00699] Avg episode reward: [(0, '4.240')] | |
| [2023-02-25 13:38:54,196][00699] Fps is (10 sec: 4505.6, 60 sec: 2457.6, 300 sec: 2457.6). Total num frames: 110592. Throughput: 0: 624.9. Samples: 28122. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:38:54,201][00699] Avg episode reward: [(0, '4.268')] | |
| [2023-02-25 13:38:54,212][12789] Saving new best policy, reward=4.268! | |
| [2023-02-25 13:38:59,197][00699] Fps is (10 sec: 2867.2, 60 sec: 2375.7, 300 sec: 2375.7). Total num frames: 118784. Throughput: 0: 668.4. Samples: 30078. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:38:59,206][00699] Avg episode reward: [(0, '4.382')] | |
| [2023-02-25 13:38:59,265][12789] Saving new best policy, reward=4.382! | |
| [2023-02-25 13:38:59,287][12803] Updated weights for policy 0, policy_version 30 (0.0026) | |
| [2023-02-25 13:39:04,196][00699] Fps is (10 sec: 2048.0, 60 sec: 2383.1, 300 sec: 2383.1). Total num frames: 131072. Throughput: 0: 739.6. Samples: 33310. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:39:04,204][00699] Avg episode reward: [(0, '4.457')] | |
| [2023-02-25 13:39:04,206][12789] Saving new best policy, reward=4.457! | |
| [2023-02-25 13:39:09,197][00699] Fps is (10 sec: 2457.6, 60 sec: 2389.3, 300 sec: 2389.3). Total num frames: 143360. Throughput: 0: 761.1. Samples: 36912. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:39:09,203][00699] Avg episode reward: [(0, '4.486')] | |
| [2023-02-25 13:39:09,216][12789] Saving new best policy, reward=4.486! | |
| [2023-02-25 13:39:13,707][12803] Updated weights for policy 0, policy_version 40 (0.0038) | |
| [2023-02-25 13:39:14,196][00699] Fps is (10 sec: 3276.8, 60 sec: 2730.7, 300 sec: 2520.6). Total num frames: 163840. Throughput: 0: 801.7. Samples: 39660. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:39:14,202][00699] Avg episode reward: [(0, '4.470')] | |
| [2023-02-25 13:39:19,197][00699] Fps is (10 sec: 4096.1, 60 sec: 3072.0, 300 sec: 2633.1). Total num frames: 184320. Throughput: 0: 812.4. Samples: 45626. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:39:19,202][00699] Avg episode reward: [(0, '4.309')] | |
| [2023-02-25 13:39:24,199][00699] Fps is (10 sec: 3276.1, 60 sec: 3276.7, 300 sec: 2621.4). Total num frames: 196608. Throughput: 0: 812.0. Samples: 49914. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) | |
| [2023-02-25 13:39:24,202][00699] Avg episode reward: [(0, '4.356')] | |
| [2023-02-25 13:39:26,654][12803] Updated weights for policy 0, policy_version 50 (0.0022) | |
| [2023-02-25 13:39:29,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3276.9, 300 sec: 2662.4). Total num frames: 212992. Throughput: 0: 810.8. Samples: 51992. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:39:29,199][00699] Avg episode reward: [(0, '4.447')] | |
| [2023-02-25 13:39:34,196][00699] Fps is (10 sec: 4096.9, 60 sec: 3413.3, 300 sec: 2794.9). Total num frames: 237568. Throughput: 0: 813.2. Samples: 58658. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:39:34,204][00699] Avg episode reward: [(0, '4.415')] | |
| [2023-02-25 13:39:36,075][12803] Updated weights for policy 0, policy_version 60 (0.0018) | |
| [2023-02-25 13:39:39,198][00699] Fps is (10 sec: 4095.5, 60 sec: 3413.5, 300 sec: 2821.6). Total num frames: 253952. Throughput: 0: 807.1. Samples: 64442. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:39:39,206][00699] Avg episode reward: [(0, '4.347')] | |
| [2023-02-25 13:39:44,199][00699] Fps is (10 sec: 2866.5, 60 sec: 3344.9, 300 sec: 2802.5). Total num frames: 266240. Throughput: 0: 808.7. Samples: 66470. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:39:44,206][00699] Avg episode reward: [(0, '4.475')] | |
| [2023-02-25 13:39:49,198][00699] Fps is (10 sec: 2867.2, 60 sec: 3208.5, 300 sec: 2826.2). Total num frames: 282624. Throughput: 0: 825.4. Samples: 70452. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:39:49,205][00699] Avg episode reward: [(0, '4.382')] | |
| [2023-02-25 13:39:49,218][12789] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000069_282624.pth... | |
| [2023-02-25 13:39:49,889][12803] Updated weights for policy 0, policy_version 70 (0.0017) | |
| [2023-02-25 13:39:54,196][00699] Fps is (10 sec: 3687.2, 60 sec: 3208.5, 300 sec: 2886.7). Total num frames: 303104. Throughput: 0: 882.8. Samples: 76640. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:39:54,203][00699] Avg episode reward: [(0, '4.416')] | |
| [2023-02-25 13:39:59,196][00699] Fps is (10 sec: 4096.5, 60 sec: 3413.3, 300 sec: 2941.7). Total num frames: 323584. Throughput: 0: 890.4. Samples: 79726. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) | |
| [2023-02-25 13:39:59,199][00699] Avg episode reward: [(0, '4.471')] | |
| [2023-02-25 13:40:00,583][12803] Updated weights for policy 0, policy_version 80 (0.0013) | |
| [2023-02-25 13:40:04,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3413.3, 300 sec: 2920.6). Total num frames: 335872. Throughput: 0: 854.7. Samples: 84088. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) | |
| [2023-02-25 13:40:04,208][00699] Avg episode reward: [(0, '4.442')] | |
| [2023-02-25 13:40:09,197][00699] Fps is (10 sec: 2457.6, 60 sec: 3413.3, 300 sec: 2901.3). Total num frames: 348160. Throughput: 0: 852.1. Samples: 88258. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:40:09,203][00699] Avg episode reward: [(0, '4.489')] | |
| [2023-02-25 13:40:09,218][12789] Saving new best policy, reward=4.489! | |
| [2023-02-25 13:40:13,388][12803] Updated weights for policy 0, policy_version 90 (0.0026) | |
| [2023-02-25 13:40:14,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3413.3, 300 sec: 2949.1). Total num frames: 368640. Throughput: 0: 872.2. Samples: 91242. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:40:14,199][00699] Avg episode reward: [(0, '4.461')] | |
| [2023-02-25 13:40:19,199][00699] Fps is (10 sec: 4095.0, 60 sec: 3413.2, 300 sec: 2993.2). Total num frames: 389120. Throughput: 0: 863.8. Samples: 97530. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:40:19,207][00699] Avg episode reward: [(0, '4.482')] | |
| [2023-02-25 13:40:24,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3413.5, 300 sec: 2973.4). Total num frames: 401408. Throughput: 0: 831.8. Samples: 101874. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:40:24,203][00699] Avg episode reward: [(0, '4.534')] | |
| [2023-02-25 13:40:24,267][12789] Saving new best policy, reward=4.534! | |
| [2023-02-25 13:40:25,830][12803] Updated weights for policy 0, policy_version 100 (0.0014) | |
| [2023-02-25 13:40:29,196][00699] Fps is (10 sec: 2868.0, 60 sec: 3413.3, 300 sec: 2984.2). Total num frames: 417792. Throughput: 0: 828.8. Samples: 103766. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:40:29,203][00699] Avg episode reward: [(0, '4.524')] | |
| [2023-02-25 13:40:34,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3345.1, 300 sec: 3022.6). Total num frames: 438272. Throughput: 0: 865.7. Samples: 109408. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:40:34,203][00699] Avg episode reward: [(0, '4.426')] | |
| [2023-02-25 13:40:36,606][12803] Updated weights for policy 0, policy_version 110 (0.0028) | |
| [2023-02-25 13:40:39,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3413.4, 300 sec: 3058.3). Total num frames: 458752. Throughput: 0: 873.7. Samples: 115956. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:40:39,199][00699] Avg episode reward: [(0, '4.391')] | |
| [2023-02-25 13:40:44,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3481.7, 300 sec: 3065.4). Total num frames: 475136. Throughput: 0: 852.6. Samples: 118092. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:40:44,199][00699] Avg episode reward: [(0, '4.386')] | |
| [2023-02-25 13:40:49,197][00699] Fps is (10 sec: 2867.1, 60 sec: 3413.4, 300 sec: 3046.4). Total num frames: 487424. Throughput: 0: 847.5. Samples: 122224. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:40:49,201][00699] Avg episode reward: [(0, '4.395')] | |
| [2023-02-25 13:40:49,737][12803] Updated weights for policy 0, policy_version 120 (0.0034) | |
| [2023-02-25 13:40:54,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3413.3, 300 sec: 3078.2). Total num frames: 507904. Throughput: 0: 887.2. Samples: 128182. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:40:54,204][00699] Avg episode reward: [(0, '4.368')] | |
| [2023-02-25 13:40:59,196][00699] Fps is (10 sec: 4096.1, 60 sec: 3413.3, 300 sec: 3108.1). Total num frames: 528384. Throughput: 0: 893.0. Samples: 131428. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:40:59,198][00699] Avg episode reward: [(0, '4.583')] | |
| [2023-02-25 13:40:59,214][12789] Saving new best policy, reward=4.583! | |
| [2023-02-25 13:40:59,218][12803] Updated weights for policy 0, policy_version 130 (0.0026) | |
| [2023-02-25 13:41:04,198][00699] Fps is (10 sec: 3685.8, 60 sec: 3481.5, 300 sec: 3112.9). Total num frames: 544768. Throughput: 0: 864.6. Samples: 136438. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:41:04,206][00699] Avg episode reward: [(0, '4.595')] | |
| [2023-02-25 13:41:04,208][12789] Saving new best policy, reward=4.595! | |
| [2023-02-25 13:41:09,197][00699] Fps is (10 sec: 2867.0, 60 sec: 3481.6, 300 sec: 3094.7). Total num frames: 557056. Throughput: 0: 857.6. Samples: 140466. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:41:09,205][00699] Avg episode reward: [(0, '4.544')] | |
| [2023-02-25 13:41:12,959][12803] Updated weights for policy 0, policy_version 140 (0.0013) | |
| [2023-02-25 13:41:14,196][00699] Fps is (10 sec: 3277.3, 60 sec: 3481.6, 300 sec: 3121.8). Total num frames: 577536. Throughput: 0: 871.3. Samples: 142976. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:41:14,202][00699] Avg episode reward: [(0, '4.354')] | |
| [2023-02-25 13:41:19,197][00699] Fps is (10 sec: 4096.2, 60 sec: 3481.7, 300 sec: 3147.4). Total num frames: 598016. Throughput: 0: 892.4. Samples: 149564. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:41:19,202][00699] Avg episode reward: [(0, '4.352')] | |
| [2023-02-25 13:41:23,498][12803] Updated weights for policy 0, policy_version 150 (0.0012) | |
| [2023-02-25 13:41:24,214][00699] Fps is (10 sec: 3679.9, 60 sec: 3548.8, 300 sec: 3150.5). Total num frames: 614400. Throughput: 0: 858.0. Samples: 154580. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) | |
| [2023-02-25 13:41:24,217][00699] Avg episode reward: [(0, '4.365')] | |
| [2023-02-25 13:41:29,197][00699] Fps is (10 sec: 2867.0, 60 sec: 3481.5, 300 sec: 3133.4). Total num frames: 626688. Throughput: 0: 855.9. Samples: 156610. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) | |
| [2023-02-25 13:41:29,204][00699] Avg episode reward: [(0, '4.388')] | |
| [2023-02-25 13:41:34,196][00699] Fps is (10 sec: 3282.5, 60 sec: 3481.6, 300 sec: 3156.9). Total num frames: 647168. Throughput: 0: 872.9. Samples: 161504. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:41:34,199][00699] Avg episode reward: [(0, '4.522')] | |
| [2023-02-25 13:41:35,799][12803] Updated weights for policy 0, policy_version 160 (0.0020) | |
| [2023-02-25 13:41:39,196][00699] Fps is (10 sec: 4096.4, 60 sec: 3481.6, 300 sec: 3179.3). Total num frames: 667648. Throughput: 0: 886.0. Samples: 168054. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:41:39,199][00699] Avg episode reward: [(0, '4.532')] | |
| [2023-02-25 13:41:44,197][00699] Fps is (10 sec: 3686.1, 60 sec: 3481.6, 300 sec: 3181.5). Total num frames: 684032. Throughput: 0: 873.9. Samples: 170756. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:41:44,200][00699] Avg episode reward: [(0, '4.544')] | |
| [2023-02-25 13:41:47,745][12803] Updated weights for policy 0, policy_version 170 (0.0024) | |
| [2023-02-25 13:41:49,197][00699] Fps is (10 sec: 2867.1, 60 sec: 3481.6, 300 sec: 3165.1). Total num frames: 696320. Throughput: 0: 852.1. Samples: 174782. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:41:49,203][00699] Avg episode reward: [(0, '4.440')] | |
| [2023-02-25 13:41:49,219][12789] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000170_696320.pth... | |
| [2023-02-25 13:41:49,435][12789] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000000_0.pth | |
| [2023-02-25 13:41:54,196][00699] Fps is (10 sec: 3277.1, 60 sec: 3481.6, 300 sec: 3185.8). Total num frames: 716800. Throughput: 0: 872.1. Samples: 179708. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:41:54,200][00699] Avg episode reward: [(0, '4.546')] | |
| [2023-02-25 13:41:59,104][12803] Updated weights for policy 0, policy_version 180 (0.0024) | |
| [2023-02-25 13:41:59,196][00699] Fps is (10 sec: 4096.1, 60 sec: 3481.6, 300 sec: 3205.6). Total num frames: 737280. Throughput: 0: 883.7. Samples: 182742. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:41:59,199][00699] Avg episode reward: [(0, '4.559')] | |
| [2023-02-25 13:42:04,197][00699] Fps is (10 sec: 3686.3, 60 sec: 3481.7, 300 sec: 3207.1). Total num frames: 753664. Throughput: 0: 862.5. Samples: 188378. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) | |
| [2023-02-25 13:42:04,201][00699] Avg episode reward: [(0, '4.434')] | |
| [2023-02-25 13:42:09,201][00699] Fps is (10 sec: 2866.0, 60 sec: 3481.4, 300 sec: 3191.4). Total num frames: 765952. Throughput: 0: 834.1. Samples: 192102. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:42:09,210][00699] Avg episode reward: [(0, '4.536')] | |
| [2023-02-25 13:42:14,202][00699] Fps is (10 sec: 2047.0, 60 sec: 3276.5, 300 sec: 3159.7). Total num frames: 774144. Throughput: 0: 822.5. Samples: 193626. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:42:14,207][00699] Avg episode reward: [(0, '4.519')] | |
| [2023-02-25 13:42:14,734][12803] Updated weights for policy 0, policy_version 190 (0.0020) | |
| [2023-02-25 13:42:19,196][00699] Fps is (10 sec: 2048.8, 60 sec: 3140.3, 300 sec: 3145.7). Total num frames: 786432. Throughput: 0: 793.6. Samples: 197218. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:42:19,205][00699] Avg episode reward: [(0, '4.542')] | |
| [2023-02-25 13:42:24,196][00699] Fps is (10 sec: 3278.5, 60 sec: 3209.5, 300 sec: 3164.4). Total num frames: 806912. Throughput: 0: 771.0. Samples: 202748. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:42:24,203][00699] Avg episode reward: [(0, '4.444')] | |
| [2023-02-25 13:42:27,963][12803] Updated weights for policy 0, policy_version 200 (0.0015) | |
| [2023-02-25 13:42:29,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3208.6, 300 sec: 3150.8). Total num frames: 819200. Throughput: 0: 757.3. Samples: 204834. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:42:29,201][00699] Avg episode reward: [(0, '4.356')] | |
| [2023-02-25 13:42:34,197][00699] Fps is (10 sec: 2867.2, 60 sec: 3140.3, 300 sec: 3153.1). Total num frames: 835584. Throughput: 0: 757.6. Samples: 208872. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:42:34,206][00699] Avg episode reward: [(0, '4.367')] | |
| [2023-02-25 13:42:39,197][00699] Fps is (10 sec: 3686.4, 60 sec: 3140.3, 300 sec: 3170.6). Total num frames: 856064. Throughput: 0: 779.8. Samples: 214798. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:42:39,199][00699] Avg episode reward: [(0, '4.418')] | |
| [2023-02-25 13:42:39,787][12803] Updated weights for policy 0, policy_version 210 (0.0012) | |
| [2023-02-25 13:42:44,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3208.6, 300 sec: 3187.4). Total num frames: 876544. Throughput: 0: 784.2. Samples: 218032. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:42:44,199][00699] Avg episode reward: [(0, '4.571')] | |
| [2023-02-25 13:42:49,198][00699] Fps is (10 sec: 3685.7, 60 sec: 3276.7, 300 sec: 3189.0). Total num frames: 892928. Throughput: 0: 770.9. Samples: 223068. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:42:49,203][00699] Avg episode reward: [(0, '4.552')] | |
| [2023-02-25 13:42:52,323][12803] Updated weights for policy 0, policy_version 220 (0.0023) | |
| [2023-02-25 13:42:54,197][00699] Fps is (10 sec: 2867.1, 60 sec: 3140.3, 300 sec: 3176.2). Total num frames: 905216. Throughput: 0: 776.3. Samples: 227032. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:42:54,205][00699] Avg episode reward: [(0, '4.517')] | |
| [2023-02-25 13:42:59,197][00699] Fps is (10 sec: 3277.4, 60 sec: 3140.3, 300 sec: 3192.1). Total num frames: 925696. Throughput: 0: 806.2. Samples: 229900. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:42:59,205][00699] Avg episode reward: [(0, '4.448')] | |
| [2023-02-25 13:43:02,756][12803] Updated weights for policy 0, policy_version 230 (0.0021) | |
| [2023-02-25 13:43:04,196][00699] Fps is (10 sec: 4096.1, 60 sec: 3208.6, 300 sec: 3207.4). Total num frames: 946176. Throughput: 0: 871.6. Samples: 236440. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:43:04,207][00699] Avg episode reward: [(0, '4.605')] | |
| [2023-02-25 13:43:04,209][12789] Saving new best policy, reward=4.605! | |
| [2023-02-25 13:43:09,203][00699] Fps is (10 sec: 3684.1, 60 sec: 3276.7, 300 sec: 3262.8). Total num frames: 962560. Throughput: 0: 856.7. Samples: 241306. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:43:09,206][00699] Avg episode reward: [(0, '4.533')] | |
| [2023-02-25 13:43:14,199][00699] Fps is (10 sec: 2866.5, 60 sec: 3345.2, 300 sec: 3304.5). Total num frames: 974848. Throughput: 0: 854.8. Samples: 243302. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:43:14,202][00699] Avg episode reward: [(0, '4.522')] | |
| [2023-02-25 13:43:16,165][12803] Updated weights for policy 0, policy_version 240 (0.0012) | |
| [2023-02-25 13:43:19,196][00699] Fps is (10 sec: 3278.9, 60 sec: 3481.6, 300 sec: 3374.0). Total num frames: 995328. Throughput: 0: 879.1. Samples: 248430. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:43:19,204][00699] Avg episode reward: [(0, '4.599')] | |
| [2023-02-25 13:43:24,196][00699] Fps is (10 sec: 4096.9, 60 sec: 3481.6, 300 sec: 3387.9). Total num frames: 1015808. Throughput: 0: 900.3. Samples: 255310. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:43:24,204][00699] Avg episode reward: [(0, '4.764')] | |
| [2023-02-25 13:43:24,290][12789] Saving new best policy, reward=4.764! | |
| [2023-02-25 13:43:25,257][12803] Updated weights for policy 0, policy_version 250 (0.0029) | |
| [2023-02-25 13:43:29,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3387.9). Total num frames: 1032192. Throughput: 0: 888.2. Samples: 258002. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:43:29,202][00699] Avg episode reward: [(0, '4.763')] | |
| [2023-02-25 13:43:34,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3387.9). Total num frames: 1048576. Throughput: 0: 871.7. Samples: 262294. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) | |
| [2023-02-25 13:43:34,207][00699] Avg episode reward: [(0, '4.736')] | |
| [2023-02-25 13:43:37,992][12803] Updated weights for policy 0, policy_version 260 (0.0028) | |
| [2023-02-25 13:43:39,197][00699] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3401.8). Total num frames: 1069056. Throughput: 0: 906.2. Samples: 267812. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:43:39,204][00699] Avg episode reward: [(0, '4.703')] | |
| [2023-02-25 13:43:44,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3387.9). Total num frames: 1089536. Throughput: 0: 915.5. Samples: 271096. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:43:44,205][00699] Avg episode reward: [(0, '4.586')] | |
| [2023-02-25 13:43:47,983][12803] Updated weights for policy 0, policy_version 270 (0.0018) | |
| [2023-02-25 13:43:49,197][00699] Fps is (10 sec: 3686.3, 60 sec: 3550.0, 300 sec: 3374.0). Total num frames: 1105920. Throughput: 0: 901.1. Samples: 276990. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:43:49,205][00699] Avg episode reward: [(0, '4.837')] | |
| [2023-02-25 13:43:49,220][12789] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000270_1105920.pth... | |
| [2023-02-25 13:43:49,401][12789] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000069_282624.pth | |
| [2023-02-25 13:43:49,434][12789] Saving new best policy, reward=4.837! | |
| [2023-02-25 13:43:54,197][00699] Fps is (10 sec: 3276.8, 60 sec: 3618.1, 300 sec: 3401.8). Total num frames: 1122304. Throughput: 0: 883.9. Samples: 281074. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) | |
| [2023-02-25 13:43:54,199][00699] Avg episode reward: [(0, '4.964')] | |
| [2023-02-25 13:43:54,210][12789] Saving new best policy, reward=4.964! | |
| [2023-02-25 13:43:59,197][00699] Fps is (10 sec: 3276.9, 60 sec: 3549.9, 300 sec: 3415.6). Total num frames: 1138688. Throughput: 0: 890.2. Samples: 283360. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) | |
| [2023-02-25 13:43:59,199][00699] Avg episode reward: [(0, '4.907')] | |
| [2023-02-25 13:44:00,560][12803] Updated weights for policy 0, policy_version 280 (0.0019) | |
| [2023-02-25 13:44:04,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3443.4). Total num frames: 1159168. Throughput: 0: 922.9. Samples: 289962. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) | |
| [2023-02-25 13:44:04,202][00699] Avg episode reward: [(0, '4.968')] | |
| [2023-02-25 13:44:04,207][12789] Saving new best policy, reward=4.968! | |
| [2023-02-25 13:44:09,200][00699] Fps is (10 sec: 4094.4, 60 sec: 3618.3, 300 sec: 3443.4). Total num frames: 1179648. Throughput: 0: 891.6. Samples: 295436. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:44:09,212][00699] Avg episode reward: [(0, '5.087')] | |
| [2023-02-25 13:44:09,228][12789] Saving new best policy, reward=5.087! | |
| [2023-02-25 13:44:11,973][12803] Updated weights for policy 0, policy_version 290 (0.0014) | |
| [2023-02-25 13:44:14,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3618.3, 300 sec: 3415.6). Total num frames: 1191936. Throughput: 0: 876.0. Samples: 297422. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) | |
| [2023-02-25 13:44:14,201][00699] Avg episode reward: [(0, '5.121')] | |
| [2023-02-25 13:44:14,204][12789] Saving new best policy, reward=5.121! | |
| [2023-02-25 13:44:19,196][00699] Fps is (10 sec: 2868.4, 60 sec: 3549.9, 300 sec: 3429.6). Total num frames: 1208320. Throughput: 0: 878.6. Samples: 301832. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) | |
| [2023-02-25 13:44:19,199][00699] Avg episode reward: [(0, '5.006')] | |
| [2023-02-25 13:44:23,475][12803] Updated weights for policy 0, policy_version 300 (0.0022) | |
| [2023-02-25 13:44:24,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3443.4). Total num frames: 1228800. Throughput: 0: 902.5. Samples: 308424. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:44:24,204][00699] Avg episode reward: [(0, '5.221')] | |
| [2023-02-25 13:44:24,207][12789] Saving new best policy, reward=5.221! | |
| [2023-02-25 13:44:29,197][00699] Fps is (10 sec: 4096.0, 60 sec: 3618.1, 300 sec: 3429.5). Total num frames: 1249280. Throughput: 0: 902.2. Samples: 311696. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:44:29,202][00699] Avg episode reward: [(0, '5.346')] | |
| [2023-02-25 13:44:29,214][12789] Saving new best policy, reward=5.346! | |
| [2023-02-25 13:44:34,197][00699] Fps is (10 sec: 3276.6, 60 sec: 3549.8, 300 sec: 3415.7). Total num frames: 1261568. Throughput: 0: 861.8. Samples: 315770. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:44:34,204][00699] Avg episode reward: [(0, '5.321')] | |
| [2023-02-25 13:44:36,311][12803] Updated weights for policy 0, policy_version 310 (0.0012) | |
| [2023-02-25 13:44:39,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3429.6). Total num frames: 1277952. Throughput: 0: 875.7. Samples: 320480. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:44:39,205][00699] Avg episode reward: [(0, '5.228')] | |
| [2023-02-25 13:44:44,201][00699] Fps is (10 sec: 3685.1, 60 sec: 3481.4, 300 sec: 3443.4). Total num frames: 1298432. Throughput: 0: 898.0. Samples: 323774. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:44:44,204][00699] Avg episode reward: [(0, '4.965')] | |
| [2023-02-25 13:44:46,138][12803] Updated weights for policy 0, policy_version 320 (0.0014) | |
| [2023-02-25 13:44:49,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3443.4). Total num frames: 1318912. Throughput: 0: 894.5. Samples: 330214. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) | |
| [2023-02-25 13:44:49,204][00699] Avg episode reward: [(0, '4.892')] | |
| [2023-02-25 13:44:54,196][00699] Fps is (10 sec: 3688.0, 60 sec: 3549.9, 300 sec: 3429.5). Total num frames: 1335296. Throughput: 0: 865.2. Samples: 334366. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:44:54,199][00699] Avg episode reward: [(0, '5.219')] | |
| [2023-02-25 13:44:59,197][00699] Fps is (10 sec: 2867.1, 60 sec: 3481.6, 300 sec: 3429.5). Total num frames: 1347584. Throughput: 0: 866.7. Samples: 336422. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:44:59,200][00699] Avg episode reward: [(0, '5.157')] | |
| [2023-02-25 13:44:59,565][12803] Updated weights for policy 0, policy_version 330 (0.0027) | |
| [2023-02-25 13:45:04,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3457.3). Total num frames: 1368064. Throughput: 0: 900.4. Samples: 342350. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:45:04,204][00699] Avg episode reward: [(0, '5.232')] | |
| [2023-02-25 13:45:09,148][12803] Updated weights for policy 0, policy_version 340 (0.0020) | |
| [2023-02-25 13:45:09,196][00699] Fps is (10 sec: 4505.7, 60 sec: 3550.1, 300 sec: 3471.2). Total num frames: 1392640. Throughput: 0: 894.3. Samples: 348668. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:45:09,201][00699] Avg episode reward: [(0, '5.322')] | |
| [2023-02-25 13:45:14,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3443.4). Total num frames: 1404928. Throughput: 0: 867.0. Samples: 350712. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:45:14,206][00699] Avg episode reward: [(0, '5.523')] | |
| [2023-02-25 13:45:14,211][12789] Saving new best policy, reward=5.523! | |
| [2023-02-25 13:45:19,196][00699] Fps is (10 sec: 2457.6, 60 sec: 3481.6, 300 sec: 3443.4). Total num frames: 1417216. Throughput: 0: 865.8. Samples: 354732. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:45:19,201][00699] Avg episode reward: [(0, '5.400')] | |
| [2023-02-25 13:45:22,991][12803] Updated weights for policy 0, policy_version 350 (0.0028) | |
| [2023-02-25 13:45:24,198][00699] Fps is (10 sec: 2866.7, 60 sec: 3413.2, 300 sec: 3443.4). Total num frames: 1433600. Throughput: 0: 872.1. Samples: 359726. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) | |
| [2023-02-25 13:45:24,201][00699] Avg episode reward: [(0, '5.494')] | |
| [2023-02-25 13:45:29,197][00699] Fps is (10 sec: 3276.7, 60 sec: 3345.1, 300 sec: 3429.5). Total num frames: 1449984. Throughput: 0: 845.0. Samples: 361798. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) | |
| [2023-02-25 13:45:29,201][00699] Avg episode reward: [(0, '5.438')] | |
| [2023-02-25 13:45:34,197][00699] Fps is (10 sec: 2867.7, 60 sec: 3345.1, 300 sec: 3401.8). Total num frames: 1462272. Throughput: 0: 786.4. Samples: 365600. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:45:34,201][00699] Avg episode reward: [(0, '5.156')] | |
| [2023-02-25 13:45:38,162][12803] Updated weights for policy 0, policy_version 360 (0.0027) | |
| [2023-02-25 13:45:39,197][00699] Fps is (10 sec: 2457.6, 60 sec: 3276.8, 300 sec: 3387.9). Total num frames: 1474560. Throughput: 0: 789.2. Samples: 369882. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:45:39,201][00699] Avg episode reward: [(0, '5.260')] | |
| [2023-02-25 13:45:44,196][00699] Fps is (10 sec: 3276.9, 60 sec: 3277.0, 300 sec: 3415.7). Total num frames: 1495040. Throughput: 0: 805.2. Samples: 372654. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:45:44,198][00699] Avg episode reward: [(0, '5.495')] | |
| [2023-02-25 13:45:48,350][12803] Updated weights for policy 0, policy_version 370 (0.0024) | |
| [2023-02-25 13:45:49,196][00699] Fps is (10 sec: 4096.1, 60 sec: 3276.8, 300 sec: 3415.6). Total num frames: 1515520. Throughput: 0: 821.2. Samples: 379306. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) | |
| [2023-02-25 13:45:49,204][00699] Avg episode reward: [(0, '5.517')] | |
| [2023-02-25 13:45:49,217][12789] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000370_1515520.pth... | |
| [2023-02-25 13:45:49,385][12789] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000170_696320.pth | |
| [2023-02-25 13:45:54,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3276.8, 300 sec: 3401.8). Total num frames: 1531904. Throughput: 0: 790.0. Samples: 384220. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:45:54,202][00699] Avg episode reward: [(0, '5.389')] | |
| [2023-02-25 13:45:59,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3276.8, 300 sec: 3387.9). Total num frames: 1544192. Throughput: 0: 790.9. Samples: 386304. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:45:59,204][00699] Avg episode reward: [(0, '5.361')] | |
| [2023-02-25 13:46:01,652][12803] Updated weights for policy 0, policy_version 380 (0.0019) | |
| [2023-02-25 13:46:04,197][00699] Fps is (10 sec: 3276.7, 60 sec: 3276.8, 300 sec: 3415.7). Total num frames: 1564672. Throughput: 0: 811.3. Samples: 391240. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:46:04,206][00699] Avg episode reward: [(0, '5.372')] | |
| [2023-02-25 13:46:09,199][00699] Fps is (10 sec: 4095.1, 60 sec: 3208.4, 300 sec: 3415.6). Total num frames: 1585152. Throughput: 0: 845.1. Samples: 397758. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:46:09,205][00699] Avg episode reward: [(0, '5.069')] | |
| [2023-02-25 13:46:11,502][12803] Updated weights for policy 0, policy_version 390 (0.0012) | |
| [2023-02-25 13:46:14,196][00699] Fps is (10 sec: 3686.5, 60 sec: 3276.8, 300 sec: 3401.8). Total num frames: 1601536. Throughput: 0: 860.2. Samples: 400506. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:46:14,199][00699] Avg episode reward: [(0, '5.066')] | |
| [2023-02-25 13:46:19,199][00699] Fps is (10 sec: 3276.7, 60 sec: 3344.9, 300 sec: 3401.9). Total num frames: 1617920. Throughput: 0: 867.3. Samples: 404632. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:46:19,202][00699] Avg episode reward: [(0, '5.093')] | |
| [2023-02-25 13:46:24,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3345.2, 300 sec: 3415.7). Total num frames: 1634304. Throughput: 0: 885.7. Samples: 409738. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:46:24,199][00699] Avg episode reward: [(0, '5.133')] | |
| [2023-02-25 13:46:24,714][12803] Updated weights for policy 0, policy_version 400 (0.0026) | |
| [2023-02-25 13:46:29,196][00699] Fps is (10 sec: 3687.4, 60 sec: 3413.4, 300 sec: 3415.6). Total num frames: 1654784. Throughput: 0: 894.7. Samples: 412916. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:46:29,199][00699] Avg episode reward: [(0, '5.210')] | |
| [2023-02-25 13:46:34,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3415.6). Total num frames: 1675264. Throughput: 0: 879.4. Samples: 418880. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:46:34,199][00699] Avg episode reward: [(0, '5.206')] | |
| [2023-02-25 13:46:35,208][12803] Updated weights for policy 0, policy_version 410 (0.0018) | |
| [2023-02-25 13:46:39,203][00699] Fps is (10 sec: 3274.8, 60 sec: 3549.5, 300 sec: 3401.7). Total num frames: 1687552. Throughput: 0: 863.1. Samples: 423066. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:46:39,220][00699] Avg episode reward: [(0, '5.233')] | |
| [2023-02-25 13:46:44,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3415.6). Total num frames: 1703936. Throughput: 0: 863.1. Samples: 425142. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:46:44,204][00699] Avg episode reward: [(0, '4.942')] | |
| [2023-02-25 13:46:47,222][12803] Updated weights for policy 0, policy_version 420 (0.0018) | |
| [2023-02-25 13:46:49,196][00699] Fps is (10 sec: 3688.7, 60 sec: 3481.6, 300 sec: 3415.6). Total num frames: 1724416. Throughput: 0: 893.7. Samples: 431456. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:46:49,204][00699] Avg episode reward: [(0, '5.188')] | |
| [2023-02-25 13:46:54,197][00699] Fps is (10 sec: 4095.9, 60 sec: 3549.9, 300 sec: 3415.6). Total num frames: 1744896. Throughput: 0: 872.4. Samples: 437014. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:46:54,200][00699] Avg episode reward: [(0, '5.318')] | |
| [2023-02-25 13:46:59,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3401.8). Total num frames: 1757184. Throughput: 0: 857.0. Samples: 439070. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:46:59,200][00699] Avg episode reward: [(0, '5.614')] | |
| [2023-02-25 13:46:59,215][12789] Saving new best policy, reward=5.614! | |
| [2023-02-25 13:46:59,725][12803] Updated weights for policy 0, policy_version 430 (0.0038) | |
| [2023-02-25 13:47:04,196][00699] Fps is (10 sec: 2867.3, 60 sec: 3481.6, 300 sec: 3415.7). Total num frames: 1773568. Throughput: 0: 858.9. Samples: 443282. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:47:04,201][00699] Avg episode reward: [(0, '5.488')] | |
| [2023-02-25 13:47:09,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3481.7, 300 sec: 3457.4). Total num frames: 1794048. Throughput: 0: 891.8. Samples: 449868. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:47:09,199][00699] Avg episode reward: [(0, '5.686')] | |
| [2023-02-25 13:47:09,208][12789] Saving new best policy, reward=5.686! | |
| [2023-02-25 13:47:10,549][12803] Updated weights for policy 0, policy_version 440 (0.0022) | |
| [2023-02-25 13:47:14,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3485.1). Total num frames: 1814528. Throughput: 0: 886.3. Samples: 452800. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:47:14,200][00699] Avg episode reward: [(0, '5.653')] | |
| [2023-02-25 13:47:19,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3481.8, 300 sec: 3457.3). Total num frames: 1826816. Throughput: 0: 849.6. Samples: 457114. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:47:19,202][00699] Avg episode reward: [(0, '5.435')] | |
| [2023-02-25 13:47:23,981][12803] Updated weights for policy 0, policy_version 450 (0.0036) | |
| [2023-02-25 13:47:24,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3471.2). Total num frames: 1843200. Throughput: 0: 852.7. Samples: 461430. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:47:24,205][00699] Avg episode reward: [(0, '5.554')] | |
| [2023-02-25 13:47:29,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3485.1). Total num frames: 1863680. Throughput: 0: 877.7. Samples: 464638. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:47:29,203][00699] Avg episode reward: [(0, '5.407')] | |
| [2023-02-25 13:47:33,456][12803] Updated weights for policy 0, policy_version 460 (0.0023) | |
| [2023-02-25 13:47:34,198][00699] Fps is (10 sec: 4095.5, 60 sec: 3481.5, 300 sec: 3485.1). Total num frames: 1884160. Throughput: 0: 882.4. Samples: 471164. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:47:34,206][00699] Avg episode reward: [(0, '5.426')] | |
| [2023-02-25 13:47:39,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3482.0, 300 sec: 3457.3). Total num frames: 1896448. Throughput: 0: 852.9. Samples: 475396. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:47:39,201][00699] Avg episode reward: [(0, '5.364')] | |
| [2023-02-25 13:47:44,196][00699] Fps is (10 sec: 2867.6, 60 sec: 3481.6, 300 sec: 3457.3). Total num frames: 1912832. Throughput: 0: 852.0. Samples: 477412. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:47:44,204][00699] Avg episode reward: [(0, '5.431')] | |
| [2023-02-25 13:47:46,683][12803] Updated weights for policy 0, policy_version 470 (0.0019) | |
| [2023-02-25 13:47:49,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3485.1). Total num frames: 1933312. Throughput: 0: 890.3. Samples: 483344. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:47:49,205][00699] Avg episode reward: [(0, '5.595')] | |
| [2023-02-25 13:47:49,219][12789] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000472_1933312.pth... | |
| [2023-02-25 13:47:49,371][12789] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000270_1105920.pth | |
| [2023-02-25 13:47:54,201][00699] Fps is (10 sec: 4094.2, 60 sec: 3481.4, 300 sec: 3485.0). Total num frames: 1953792. Throughput: 0: 884.1. Samples: 489654. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) | |
| [2023-02-25 13:47:54,207][00699] Avg episode reward: [(0, '5.797')] | |
| [2023-02-25 13:47:54,212][12789] Saving new best policy, reward=5.797! | |
| [2023-02-25 13:47:57,676][12803] Updated weights for policy 0, policy_version 480 (0.0015) | |
| [2023-02-25 13:47:59,202][00699] Fps is (10 sec: 3684.5, 60 sec: 3549.6, 300 sec: 3471.1). Total num frames: 1970176. Throughput: 0: 864.4. Samples: 491702. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:47:59,205][00699] Avg episode reward: [(0, '5.744')] | |
| [2023-02-25 13:48:04,201][00699] Fps is (10 sec: 2867.0, 60 sec: 3481.3, 300 sec: 3457.3). Total num frames: 1982464. Throughput: 0: 863.2. Samples: 495960. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:48:04,204][00699] Avg episode reward: [(0, '5.671')] | |
| [2023-02-25 13:48:09,196][00699] Fps is (10 sec: 3278.5, 60 sec: 3481.6, 300 sec: 3485.1). Total num frames: 2002944. Throughput: 0: 905.2. Samples: 502166. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:48:09,205][00699] Avg episode reward: [(0, '5.494')] | |
| [2023-02-25 13:48:09,258][12803] Updated weights for policy 0, policy_version 490 (0.0016) | |
| [2023-02-25 13:48:14,199][00699] Fps is (10 sec: 4506.7, 60 sec: 3549.7, 300 sec: 3498.9). Total num frames: 2027520. Throughput: 0: 906.2. Samples: 505418. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:48:14,207][00699] Avg episode reward: [(0, '5.479')] | |
| [2023-02-25 13:48:19,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3471.2). Total num frames: 2039808. Throughput: 0: 873.8. Samples: 510486. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:48:19,201][00699] Avg episode reward: [(0, '5.613')] | |
| [2023-02-25 13:48:21,142][12803] Updated weights for policy 0, policy_version 500 (0.0012) | |
| [2023-02-25 13:48:24,198][00699] Fps is (10 sec: 2867.5, 60 sec: 3549.8, 300 sec: 3471.2). Total num frames: 2056192. Throughput: 0: 872.7. Samples: 514668. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:48:24,203][00699] Avg episode reward: [(0, '5.812')] | |
| [2023-02-25 13:48:24,207][12789] Saving new best policy, reward=5.812! | |
| [2023-02-25 13:48:29,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3485.1). Total num frames: 2076672. Throughput: 0: 894.7. Samples: 517672. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:48:29,205][00699] Avg episode reward: [(0, '5.924')] | |
| [2023-02-25 13:48:29,217][12789] Saving new best policy, reward=5.924! | |
| [2023-02-25 13:48:31,972][12803] Updated weights for policy 0, policy_version 510 (0.0021) | |
| [2023-02-25 13:48:34,196][00699] Fps is (10 sec: 4096.5, 60 sec: 3549.9, 300 sec: 3485.1). Total num frames: 2097152. Throughput: 0: 903.6. Samples: 524006. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:48:34,199][00699] Avg episode reward: [(0, '6.142')] | |
| [2023-02-25 13:48:34,202][12789] Saving new best policy, reward=6.142! | |
| [2023-02-25 13:48:39,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3457.3). Total num frames: 2109440. Throughput: 0: 850.1. Samples: 527906. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:48:39,199][00699] Avg episode reward: [(0, '5.880')] | |
| [2023-02-25 13:48:44,199][00699] Fps is (10 sec: 2047.5, 60 sec: 3413.2, 300 sec: 3429.5). Total num frames: 2117632. Throughput: 0: 841.5. Samples: 529566. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:48:44,201][00699] Avg episode reward: [(0, '6.047')] | |
| [2023-02-25 13:48:48,142][12803] Updated weights for policy 0, policy_version 520 (0.0018) | |
| [2023-02-25 13:48:49,197][00699] Fps is (10 sec: 2047.9, 60 sec: 3276.8, 300 sec: 3415.6). Total num frames: 2129920. Throughput: 0: 822.7. Samples: 532980. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:48:49,200][00699] Avg episode reward: [(0, '6.281')] | |
| [2023-02-25 13:48:49,217][12789] Saving new best policy, reward=6.281! | |
| [2023-02-25 13:48:54,201][00699] Fps is (10 sec: 3276.2, 60 sec: 3276.8, 300 sec: 3429.5). Total num frames: 2150400. Throughput: 0: 804.8. Samples: 538386. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:48:54,209][00699] Avg episode reward: [(0, '6.131')] | |
| [2023-02-25 13:48:58,539][12803] Updated weights for policy 0, policy_version 530 (0.0026) | |
| [2023-02-25 13:48:59,196][00699] Fps is (10 sec: 4096.1, 60 sec: 3345.4, 300 sec: 3429.5). Total num frames: 2170880. Throughput: 0: 805.4. Samples: 541658. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:48:59,206][00699] Avg episode reward: [(0, '6.068')] | |
| [2023-02-25 13:49:04,202][00699] Fps is (10 sec: 3686.0, 60 sec: 3413.3, 300 sec: 3415.6). Total num frames: 2187264. Throughput: 0: 814.7. Samples: 547152. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) | |
| [2023-02-25 13:49:04,205][00699] Avg episode reward: [(0, '6.140')] | |
| [2023-02-25 13:49:09,197][00699] Fps is (10 sec: 2867.2, 60 sec: 3276.8, 300 sec: 3415.6). Total num frames: 2199552. Throughput: 0: 813.0. Samples: 551254. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:49:09,199][00699] Avg episode reward: [(0, '6.317')] | |
| [2023-02-25 13:49:09,253][12789] Saving new best policy, reward=6.317! | |
| [2023-02-25 13:49:11,967][12803] Updated weights for policy 0, policy_version 540 (0.0031) | |
| [2023-02-25 13:49:14,199][00699] Fps is (10 sec: 3277.8, 60 sec: 3208.5, 300 sec: 3429.5). Total num frames: 2220032. Throughput: 0: 798.5. Samples: 553608. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:49:14,207][00699] Avg episode reward: [(0, '6.686')] | |
| [2023-02-25 13:49:14,210][12789] Saving new best policy, reward=6.686! | |
| [2023-02-25 13:49:19,203][00699] Fps is (10 sec: 4093.3, 60 sec: 3344.7, 300 sec: 3429.5). Total num frames: 2240512. Throughput: 0: 800.2. Samples: 560022. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:49:19,206][00699] Avg episode reward: [(0, '6.619')] | |
| [2023-02-25 13:49:21,678][12803] Updated weights for policy 0, policy_version 550 (0.0019) | |
| [2023-02-25 13:49:24,196][00699] Fps is (10 sec: 3687.2, 60 sec: 3345.1, 300 sec: 3415.6). Total num frames: 2256896. Throughput: 0: 832.6. Samples: 565372. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:49:24,199][00699] Avg episode reward: [(0, '7.067')] | |
| [2023-02-25 13:49:24,207][12789] Saving new best policy, reward=7.067! | |
| [2023-02-25 13:49:29,202][00699] Fps is (10 sec: 3277.2, 60 sec: 3276.5, 300 sec: 3429.5). Total num frames: 2273280. Throughput: 0: 841.9. Samples: 567454. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:49:29,209][00699] Avg episode reward: [(0, '6.617')] | |
| [2023-02-25 13:49:34,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3208.5, 300 sec: 3429.5). Total num frames: 2289664. Throughput: 0: 871.7. Samples: 572208. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:49:34,204][00699] Avg episode reward: [(0, '6.969')] | |
| [2023-02-25 13:49:34,714][12803] Updated weights for policy 0, policy_version 560 (0.0026) | |
| [2023-02-25 13:49:39,196][00699] Fps is (10 sec: 3688.3, 60 sec: 3345.1, 300 sec: 3429.6). Total num frames: 2310144. Throughput: 0: 900.7. Samples: 578912. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:49:39,208][00699] Avg episode reward: [(0, '6.914')] | |
| [2023-02-25 13:49:44,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3550.0, 300 sec: 3429.5). Total num frames: 2330624. Throughput: 0: 895.4. Samples: 581950. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:49:44,200][00699] Avg episode reward: [(0, '7.068')] | |
| [2023-02-25 13:49:45,320][12803] Updated weights for policy 0, policy_version 570 (0.0024) | |
| [2023-02-25 13:49:49,197][00699] Fps is (10 sec: 3276.6, 60 sec: 3549.8, 300 sec: 3415.6). Total num frames: 2342912. Throughput: 0: 866.2. Samples: 586128. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:49:49,199][00699] Avg episode reward: [(0, '6.805')] | |
| [2023-02-25 13:49:49,218][12789] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000572_2342912.pth... | |
| [2023-02-25 13:49:49,443][12789] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000370_1515520.pth | |
| [2023-02-25 13:49:54,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3481.8, 300 sec: 3429.5). Total num frames: 2359296. Throughput: 0: 884.8. Samples: 591070. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) | |
| [2023-02-25 13:49:54,202][00699] Avg episode reward: [(0, '6.399')] | |
| [2023-02-25 13:49:57,255][12803] Updated weights for policy 0, policy_version 580 (0.0024) | |
| [2023-02-25 13:49:59,196][00699] Fps is (10 sec: 4096.3, 60 sec: 3549.9, 300 sec: 3443.4). Total num frames: 2383872. Throughput: 0: 905.0. Samples: 594332. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:49:59,203][00699] Avg episode reward: [(0, '6.943')] | |
| [2023-02-25 13:50:04,197][00699] Fps is (10 sec: 4095.9, 60 sec: 3550.2, 300 sec: 3415.6). Total num frames: 2400256. Throughput: 0: 894.7. Samples: 600278. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:50:04,204][00699] Avg episode reward: [(0, '7.075')] | |
| [2023-02-25 13:50:04,208][12789] Saving new best policy, reward=7.075! | |
| [2023-02-25 13:50:09,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3549.9, 300 sec: 3415.6). Total num frames: 2412544. Throughput: 0: 865.2. Samples: 604306. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:50:09,204][00699] Avg episode reward: [(0, '7.393')] | |
| [2023-02-25 13:50:09,216][12789] Saving new best policy, reward=7.393! | |
| [2023-02-25 13:50:09,742][12803] Updated weights for policy 0, policy_version 590 (0.0022) | |
| [2023-02-25 13:50:14,197][00699] Fps is (10 sec: 2867.2, 60 sec: 3481.7, 300 sec: 3429.5). Total num frames: 2428928. Throughput: 0: 866.6. Samples: 606448. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:50:14,199][00699] Avg episode reward: [(0, '7.025')] | |
| [2023-02-25 13:50:19,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3550.3, 300 sec: 3457.3). Total num frames: 2453504. Throughput: 0: 907.4. Samples: 613042. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:50:19,205][00699] Avg episode reward: [(0, '7.622')] | |
| [2023-02-25 13:50:19,215][12789] Saving new best policy, reward=7.622! | |
| [2023-02-25 13:50:19,867][12803] Updated weights for policy 0, policy_version 600 (0.0030) | |
| [2023-02-25 13:50:24,196][00699] Fps is (10 sec: 4505.7, 60 sec: 3618.1, 300 sec: 3471.2). Total num frames: 2473984. Throughput: 0: 890.0. Samples: 618964. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:50:24,199][00699] Avg episode reward: [(0, '8.359')] | |
| [2023-02-25 13:50:24,202][12789] Saving new best policy, reward=8.359! | |
| [2023-02-25 13:50:29,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3550.2, 300 sec: 3471.2). Total num frames: 2486272. Throughput: 0: 868.7. Samples: 621042. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:50:29,203][00699] Avg episode reward: [(0, '8.629')] | |
| [2023-02-25 13:50:29,215][12789] Saving new best policy, reward=8.629! | |
| [2023-02-25 13:50:33,115][12803] Updated weights for policy 0, policy_version 610 (0.0020) | |
| [2023-02-25 13:50:34,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3549.9, 300 sec: 3485.1). Total num frames: 2502656. Throughput: 0: 868.2. Samples: 625196. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:50:34,204][00699] Avg episode reward: [(0, '8.166')] | |
| [2023-02-25 13:50:39,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3485.1). Total num frames: 2523136. Throughput: 0: 907.3. Samples: 631900. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:50:39,206][00699] Avg episode reward: [(0, '7.996')] | |
| [2023-02-25 13:50:42,278][12803] Updated weights for policy 0, policy_version 620 (0.0014) | |
| [2023-02-25 13:50:44,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3485.1). Total num frames: 2543616. Throughput: 0: 909.4. Samples: 635256. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:50:44,203][00699] Avg episode reward: [(0, '7.996')] | |
| [2023-02-25 13:50:49,197][00699] Fps is (10 sec: 3686.3, 60 sec: 3618.2, 300 sec: 3485.1). Total num frames: 2560000. Throughput: 0: 880.7. Samples: 639908. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:50:49,204][00699] Avg episode reward: [(0, '8.128')] | |
| [2023-02-25 13:50:54,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3549.9, 300 sec: 3485.1). Total num frames: 2572288. Throughput: 0: 889.6. Samples: 644340. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:50:54,199][00699] Avg episode reward: [(0, '8.162')] | |
| [2023-02-25 13:50:55,295][12803] Updated weights for policy 0, policy_version 630 (0.0021) | |
| [2023-02-25 13:50:59,196][00699] Fps is (10 sec: 3686.5, 60 sec: 3549.9, 300 sec: 3499.0). Total num frames: 2596864. Throughput: 0: 915.8. Samples: 647660. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:50:59,199][00699] Avg episode reward: [(0, '7.966')] | |
| [2023-02-25 13:51:04,196][00699] Fps is (10 sec: 4505.6, 60 sec: 3618.1, 300 sec: 3499.0). Total num frames: 2617344. Throughput: 0: 917.9. Samples: 654348. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:51:04,201][00699] Avg episode reward: [(0, '7.839')] | |
| [2023-02-25 13:51:04,934][12803] Updated weights for policy 0, policy_version 640 (0.0026) | |
| [2023-02-25 13:51:09,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3618.1, 300 sec: 3485.1). Total num frames: 2629632. Throughput: 0: 885.6. Samples: 658818. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:51:09,202][00699] Avg episode reward: [(0, '7.454')] | |
| [2023-02-25 13:51:14,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3618.1, 300 sec: 3485.1). Total num frames: 2646016. Throughput: 0: 885.6. Samples: 660896. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:51:14,205][00699] Avg episode reward: [(0, '7.534')] | |
| [2023-02-25 13:51:17,509][12803] Updated weights for policy 0, policy_version 650 (0.0018) | |
| [2023-02-25 13:51:19,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3499.0). Total num frames: 2666496. Throughput: 0: 926.8. Samples: 666902. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:51:19,201][00699] Avg episode reward: [(0, '7.714')] | |
| [2023-02-25 13:51:24,196][00699] Fps is (10 sec: 4505.6, 60 sec: 3618.1, 300 sec: 3512.8). Total num frames: 2691072. Throughput: 0: 924.7. Samples: 673510. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:51:24,201][00699] Avg episode reward: [(0, '8.175')] | |
| [2023-02-25 13:51:27,976][12803] Updated weights for policy 0, policy_version 660 (0.0020) | |
| [2023-02-25 13:51:29,199][00699] Fps is (10 sec: 3685.6, 60 sec: 3618.0, 300 sec: 3485.0). Total num frames: 2703360. Throughput: 0: 899.2. Samples: 675724. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:51:29,201][00699] Avg episode reward: [(0, '7.850')] | |
| [2023-02-25 13:51:34,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3618.1, 300 sec: 3499.0). Total num frames: 2719744. Throughput: 0: 894.9. Samples: 680180. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:51:34,199][00699] Avg episode reward: [(0, '7.599')] | |
| [2023-02-25 13:51:39,196][00699] Fps is (10 sec: 3687.2, 60 sec: 3618.1, 300 sec: 3512.8). Total num frames: 2740224. Throughput: 0: 934.8. Samples: 686408. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:51:39,204][00699] Avg episode reward: [(0, '7.200')] | |
| [2023-02-25 13:51:39,323][12803] Updated weights for policy 0, policy_version 670 (0.0028) | |
| [2023-02-25 13:51:44,196][00699] Fps is (10 sec: 4505.6, 60 sec: 3686.4, 300 sec: 3526.7). Total num frames: 2764800. Throughput: 0: 937.0. Samples: 689826. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:51:44,203][00699] Avg episode reward: [(0, '7.977')] | |
| [2023-02-25 13:51:49,197][00699] Fps is (10 sec: 3686.3, 60 sec: 3618.1, 300 sec: 3499.0). Total num frames: 2777088. Throughput: 0: 901.8. Samples: 694930. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:51:49,201][00699] Avg episode reward: [(0, '8.604')] | |
| [2023-02-25 13:51:49,213][12789] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000678_2777088.pth... | |
| [2023-02-25 13:51:49,363][12789] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000472_1933312.pth | |
| [2023-02-25 13:51:51,049][12803] Updated weights for policy 0, policy_version 680 (0.0012) | |
| [2023-02-25 13:51:54,196][00699] Fps is (10 sec: 2457.6, 60 sec: 3618.1, 300 sec: 3499.0). Total num frames: 2789376. Throughput: 0: 886.7. Samples: 698718. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) | |
| [2023-02-25 13:51:54,203][00699] Avg episode reward: [(0, '8.738')] | |
| [2023-02-25 13:51:54,209][12789] Saving new best policy, reward=8.738! | |
| [2023-02-25 13:51:59,196][00699] Fps is (10 sec: 2457.7, 60 sec: 3413.3, 300 sec: 3485.1). Total num frames: 2801664. Throughput: 0: 881.5. Samples: 700562. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:51:59,203][00699] Avg episode reward: [(0, '9.336')] | |
| [2023-02-25 13:51:59,215][12789] Saving new best policy, reward=9.336! | |
| [2023-02-25 13:52:04,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3345.1, 300 sec: 3471.2). Total num frames: 2818048. Throughput: 0: 842.8. Samples: 704830. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:52:04,204][00699] Avg episode reward: [(0, '9.087')] | |
| [2023-02-25 13:52:05,183][12803] Updated weights for policy 0, policy_version 690 (0.0017) | |
| [2023-02-25 13:52:09,197][00699] Fps is (10 sec: 3686.3, 60 sec: 3481.6, 300 sec: 3471.2). Total num frames: 2838528. Throughput: 0: 817.7. Samples: 710308. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:52:09,199][00699] Avg episode reward: [(0, '8.521')] | |
| [2023-02-25 13:52:14,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3413.3, 300 sec: 3471.2). Total num frames: 2850816. Throughput: 0: 815.0. Samples: 712396. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:52:14,201][00699] Avg episode reward: [(0, '8.555')] | |
| [2023-02-25 13:52:18,156][12803] Updated weights for policy 0, policy_version 700 (0.0013) | |
| [2023-02-25 13:52:19,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3413.3, 300 sec: 3485.1). Total num frames: 2871296. Throughput: 0: 826.8. Samples: 717384. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:52:19,202][00699] Avg episode reward: [(0, '8.433')] | |
| [2023-02-25 13:52:24,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3345.1, 300 sec: 3485.1). Total num frames: 2891776. Throughput: 0: 839.5. Samples: 724184. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:52:24,201][00699] Avg episode reward: [(0, '7.871')] | |
| [2023-02-25 13:52:27,496][12803] Updated weights for policy 0, policy_version 710 (0.0022) | |
| [2023-02-25 13:52:29,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3481.7, 300 sec: 3485.1). Total num frames: 2912256. Throughput: 0: 833.7. Samples: 727342. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:52:29,200][00699] Avg episode reward: [(0, '7.437')] | |
| [2023-02-25 13:52:34,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3413.3, 300 sec: 3485.1). Total num frames: 2924544. Throughput: 0: 816.9. Samples: 731692. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:52:34,198][00699] Avg episode reward: [(0, '8.225')] | |
| [2023-02-25 13:52:39,197][00699] Fps is (10 sec: 3276.8, 60 sec: 3413.3, 300 sec: 3499.0). Total num frames: 2945024. Throughput: 0: 850.5. Samples: 736992. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:52:39,199][00699] Avg episode reward: [(0, '8.567')] | |
| [2023-02-25 13:52:39,830][12803] Updated weights for policy 0, policy_version 720 (0.0036) | |
| [2023-02-25 13:52:44,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3345.1, 300 sec: 3499.0). Total num frames: 2965504. Throughput: 0: 883.9. Samples: 740338. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:52:44,198][00699] Avg episode reward: [(0, '9.372')] | |
| [2023-02-25 13:52:44,210][12789] Saving new best policy, reward=9.372! | |
| [2023-02-25 13:52:49,197][00699] Fps is (10 sec: 4096.0, 60 sec: 3481.6, 300 sec: 3499.0). Total num frames: 2985984. Throughput: 0: 926.0. Samples: 746498. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:52:49,199][00699] Avg episode reward: [(0, '9.216')] | |
| [2023-02-25 13:52:50,089][12803] Updated weights for policy 0, policy_version 730 (0.0012) | |
| [2023-02-25 13:52:54,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3485.1). Total num frames: 2998272. Throughput: 0: 899.1. Samples: 750768. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:52:54,200][00699] Avg episode reward: [(0, '9.030')] | |
| [2023-02-25 13:52:59,197][00699] Fps is (10 sec: 3276.7, 60 sec: 3618.1, 300 sec: 3512.9). Total num frames: 3018752. Throughput: 0: 901.1. Samples: 752944. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:52:59,200][00699] Avg episode reward: [(0, '8.767')] | |
| [2023-02-25 13:53:01,859][12803] Updated weights for policy 0, policy_version 740 (0.0013) | |
| [2023-02-25 13:53:04,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3512.8). Total num frames: 3039232. Throughput: 0: 940.8. Samples: 759718. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:53:04,202][00699] Avg episode reward: [(0, '9.064')] | |
| [2023-02-25 13:53:09,196][00699] Fps is (10 sec: 4096.1, 60 sec: 3686.4, 300 sec: 3499.0). Total num frames: 3059712. Throughput: 0: 917.3. Samples: 765462. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:53:09,199][00699] Avg episode reward: [(0, '9.870')] | |
| [2023-02-25 13:53:09,212][12789] Saving new best policy, reward=9.870! | |
| [2023-02-25 13:53:13,528][12803] Updated weights for policy 0, policy_version 750 (0.0024) | |
| [2023-02-25 13:53:14,199][00699] Fps is (10 sec: 3276.1, 60 sec: 3686.3, 300 sec: 3498.9). Total num frames: 3072000. Throughput: 0: 892.8. Samples: 767520. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:53:14,209][00699] Avg episode reward: [(0, '10.192')] | |
| [2023-02-25 13:53:14,212][12789] Saving new best policy, reward=10.192! | |
| [2023-02-25 13:53:19,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3618.1, 300 sec: 3499.0). Total num frames: 3088384. Throughput: 0: 896.3. Samples: 772026. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:53:19,199][00699] Avg episode reward: [(0, '9.140')] | |
| [2023-02-25 13:53:24,024][12803] Updated weights for policy 0, policy_version 760 (0.0020) | |
| [2023-02-25 13:53:24,196][00699] Fps is (10 sec: 4096.9, 60 sec: 3686.4, 300 sec: 3512.8). Total num frames: 3112960. Throughput: 0: 928.3. Samples: 778766. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:53:24,199][00699] Avg episode reward: [(0, '8.645')] | |
| [2023-02-25 13:53:29,198][00699] Fps is (10 sec: 4095.5, 60 sec: 3618.1, 300 sec: 3498.9). Total num frames: 3129344. Throughput: 0: 929.6. Samples: 782170. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:53:29,202][00699] Avg episode reward: [(0, '8.455')] | |
| [2023-02-25 13:53:34,199][00699] Fps is (10 sec: 3276.0, 60 sec: 3686.3, 300 sec: 3512.8). Total num frames: 3145728. Throughput: 0: 890.1. Samples: 786556. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:53:34,202][00699] Avg episode reward: [(0, '8.672')] | |
| [2023-02-25 13:53:36,470][12803] Updated weights for policy 0, policy_version 770 (0.0016) | |
| [2023-02-25 13:53:39,196][00699] Fps is (10 sec: 3277.2, 60 sec: 3618.1, 300 sec: 3540.6). Total num frames: 3162112. Throughput: 0: 911.2. Samples: 791774. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:53:39,205][00699] Avg episode reward: [(0, '9.079')] | |
| [2023-02-25 13:53:44,196][00699] Fps is (10 sec: 4097.0, 60 sec: 3686.4, 300 sec: 3582.3). Total num frames: 3186688. Throughput: 0: 939.8. Samples: 795236. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:53:44,200][00699] Avg episode reward: [(0, '9.456')] | |
| [2023-02-25 13:53:45,527][12803] Updated weights for policy 0, policy_version 780 (0.0012) | |
| [2023-02-25 13:53:49,196][00699] Fps is (10 sec: 4505.6, 60 sec: 3686.4, 300 sec: 3582.3). Total num frames: 3207168. Throughput: 0: 936.8. Samples: 801874. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:53:49,201][00699] Avg episode reward: [(0, '10.128')] | |
| [2023-02-25 13:53:49,211][12789] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000783_3207168.pth... | |
| [2023-02-25 13:53:49,383][12789] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000572_2342912.pth | |
| [2023-02-25 13:53:54,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3554.5). Total num frames: 3219456. Throughput: 0: 903.7. Samples: 806128. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:53:54,199][00699] Avg episode reward: [(0, '9.263')] | |
| [2023-02-25 13:53:58,387][12803] Updated weights for policy 0, policy_version 790 (0.0012) | |
| [2023-02-25 13:53:59,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3618.1, 300 sec: 3554.6). Total num frames: 3235840. Throughput: 0: 905.2. Samples: 808250. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:53:59,200][00699] Avg episode reward: [(0, '9.499')] | |
| [2023-02-25 13:54:04,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3596.1). Total num frames: 3260416. Throughput: 0: 954.9. Samples: 814996. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:54:04,205][00699] Avg episode reward: [(0, '8.998')] | |
| [2023-02-25 13:54:07,258][12803] Updated weights for policy 0, policy_version 800 (0.0019) | |
| [2023-02-25 13:54:09,196][00699] Fps is (10 sec: 4505.6, 60 sec: 3686.4, 300 sec: 3596.2). Total num frames: 3280896. Throughput: 0: 943.8. Samples: 821238. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:54:09,201][00699] Avg episode reward: [(0, '9.859')] | |
| [2023-02-25 13:54:14,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3754.8, 300 sec: 3582.3). Total num frames: 3297280. Throughput: 0: 916.1. Samples: 823392. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:54:14,204][00699] Avg episode reward: [(0, '10.271')] | |
| [2023-02-25 13:54:14,211][12789] Saving new best policy, reward=10.271! | |
| [2023-02-25 13:54:19,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3582.3). Total num frames: 3313664. Throughput: 0: 918.4. Samples: 827882. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:54:19,204][00699] Avg episode reward: [(0, '11.178')] | |
| [2023-02-25 13:54:19,213][12789] Saving new best policy, reward=11.178! | |
| [2023-02-25 13:54:19,831][12803] Updated weights for policy 0, policy_version 810 (0.0015) | |
| [2023-02-25 13:54:24,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3596.2). Total num frames: 3334144. Throughput: 0: 954.4. Samples: 834720. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:54:24,203][00699] Avg episode reward: [(0, '12.009')] | |
| [2023-02-25 13:54:24,207][12789] Saving new best policy, reward=12.009! | |
| [2023-02-25 13:54:29,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3610.0). Total num frames: 3354624. Throughput: 0: 949.5. Samples: 837964. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:54:29,207][00699] Avg episode reward: [(0, '11.789')] | |
| [2023-02-25 13:54:29,807][12803] Updated weights for policy 0, policy_version 820 (0.0018) | |
| [2023-02-25 13:54:34,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3754.8, 300 sec: 3596.2). Total num frames: 3371008. Throughput: 0: 905.8. Samples: 842636. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:54:34,201][00699] Avg episode reward: [(0, '11.614')] | |
| [2023-02-25 13:54:39,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3582.3). Total num frames: 3387392. Throughput: 0: 921.5. Samples: 847596. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:54:39,199][00699] Avg episode reward: [(0, '11.846')] | |
| [2023-02-25 13:54:41,674][12803] Updated weights for policy 0, policy_version 830 (0.0037) | |
| [2023-02-25 13:54:44,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3623.9). Total num frames: 3411968. Throughput: 0: 950.7. Samples: 851030. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:54:44,199][00699] Avg episode reward: [(0, '12.023')] | |
| [2023-02-25 13:54:44,204][12789] Saving new best policy, reward=12.023! | |
| [2023-02-25 13:54:49,199][00699] Fps is (10 sec: 4504.6, 60 sec: 3754.5, 300 sec: 3637.8). Total num frames: 3432448. Throughput: 0: 951.0. Samples: 857792. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:54:49,201][00699] Avg episode reward: [(0, '13.010')] | |
| [2023-02-25 13:54:49,223][12789] Saving new best policy, reward=13.010! | |
| [2023-02-25 13:54:52,100][12803] Updated weights for policy 0, policy_version 840 (0.0013) | |
| [2023-02-25 13:54:54,197][00699] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3596.1). Total num frames: 3444736. Throughput: 0: 907.1. Samples: 862056. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:54:54,201][00699] Avg episode reward: [(0, '13.990')] | |
| [2023-02-25 13:54:54,205][12789] Saving new best policy, reward=13.990! | |
| [2023-02-25 13:54:59,196][00699] Fps is (10 sec: 2867.8, 60 sec: 3754.7, 300 sec: 3596.1). Total num frames: 3461120. Throughput: 0: 906.9. Samples: 864204. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:54:59,199][00699] Avg episode reward: [(0, '13.417')] | |
| [2023-02-25 13:55:03,378][12803] Updated weights for policy 0, policy_version 850 (0.0028) | |
| [2023-02-25 13:55:04,196][00699] Fps is (10 sec: 3686.5, 60 sec: 3686.4, 300 sec: 3623.9). Total num frames: 3481600. Throughput: 0: 952.8. Samples: 870758. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:55:04,203][00699] Avg episode reward: [(0, '13.524')] | |
| [2023-02-25 13:55:09,197][00699] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3651.7). Total num frames: 3506176. Throughput: 0: 948.9. Samples: 877420. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:55:09,201][00699] Avg episode reward: [(0, '13.040')] | |
| [2023-02-25 13:55:14,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3610.0). Total num frames: 3518464. Throughput: 0: 917.6. Samples: 879254. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:55:14,206][00699] Avg episode reward: [(0, '13.379')] | |
| [2023-02-25 13:55:15,292][12803] Updated weights for policy 0, policy_version 860 (0.0027) | |
| [2023-02-25 13:55:19,196][00699] Fps is (10 sec: 2457.6, 60 sec: 3618.1, 300 sec: 3582.3). Total num frames: 3530752. Throughput: 0: 889.9. Samples: 882682. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) | |
| [2023-02-25 13:55:19,199][00699] Avg episode reward: [(0, '13.830')] | |
| [2023-02-25 13:55:24,197][00699] Fps is (10 sec: 2457.5, 60 sec: 3481.6, 300 sec: 3582.3). Total num frames: 3543040. Throughput: 0: 862.4. Samples: 886402. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:55:24,201][00699] Avg episode reward: [(0, '15.119')] | |
| [2023-02-25 13:55:24,205][12789] Saving new best policy, reward=15.119! | |
| [2023-02-25 13:55:28,773][12803] Updated weights for policy 0, policy_version 870 (0.0022) | |
| [2023-02-25 13:55:29,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3596.1). Total num frames: 3563520. Throughput: 0: 858.4. Samples: 889656. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:55:29,203][00699] Avg episode reward: [(0, '16.336')] | |
| [2023-02-25 13:55:29,214][12789] Saving new best policy, reward=16.336! | |
| [2023-02-25 13:55:34,196][00699] Fps is (10 sec: 4096.1, 60 sec: 3549.9, 300 sec: 3596.1). Total num frames: 3584000. Throughput: 0: 862.5. Samples: 896602. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) | |
| [2023-02-25 13:55:34,202][00699] Avg episode reward: [(0, '15.369')] | |
| [2023-02-25 13:55:39,197][00699] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3582.3). Total num frames: 3600384. Throughput: 0: 866.6. Samples: 901054. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:55:39,202][00699] Avg episode reward: [(0, '14.383')] | |
| [2023-02-25 13:55:40,209][12803] Updated weights for policy 0, policy_version 880 (0.0022) | |
| [2023-02-25 13:55:44,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3413.3, 300 sec: 3582.3). Total num frames: 3616768. Throughput: 0: 867.6. Samples: 903244. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:55:44,204][00699] Avg episode reward: [(0, '13.175')] | |
| [2023-02-25 13:55:49,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3481.7, 300 sec: 3623.9). Total num frames: 3641344. Throughput: 0: 868.5. Samples: 909840. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:55:49,203][00699] Avg episode reward: [(0, '13.199')] | |
| [2023-02-25 13:55:49,215][12789] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000889_3641344.pth... | |
| [2023-02-25 13:55:49,350][12789] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000678_2777088.pth | |
| [2023-02-25 13:55:50,082][12803] Updated weights for policy 0, policy_version 890 (0.0023) | |
| [2023-02-25 13:55:54,196][00699] Fps is (10 sec: 4505.6, 60 sec: 3618.1, 300 sec: 3610.0). Total num frames: 3661824. Throughput: 0: 863.9. Samples: 916294. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:55:54,208][00699] Avg episode reward: [(0, '13.931')] | |
| [2023-02-25 13:55:59,196][00699] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3582.3). Total num frames: 3674112. Throughput: 0: 869.7. Samples: 918390. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:55:59,203][00699] Avg episode reward: [(0, '14.026')] | |
| [2023-02-25 13:56:02,241][12803] Updated weights for policy 0, policy_version 900 (0.0014) | |
| [2023-02-25 13:56:04,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3596.1). Total num frames: 3690496. Throughput: 0: 891.6. Samples: 922806. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:56:04,203][00699] Avg episode reward: [(0, '14.478')] | |
| [2023-02-25 13:56:09,196][00699] Fps is (10 sec: 4096.0, 60 sec: 3481.6, 300 sec: 3623.9). Total num frames: 3715072. Throughput: 0: 959.4. Samples: 929576. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:56:09,203][00699] Avg episode reward: [(0, '15.262')] | |
| [2023-02-25 13:56:11,623][12803] Updated weights for policy 0, policy_version 910 (0.0030) | |
| [2023-02-25 13:56:14,200][00699] Fps is (10 sec: 4504.2, 60 sec: 3617.9, 300 sec: 3623.9). Total num frames: 3735552. Throughput: 0: 962.2. Samples: 932956. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:56:14,211][00699] Avg episode reward: [(0, '17.171')] | |
| [2023-02-25 13:56:14,213][12789] Saving new best policy, reward=17.171! | |
| [2023-02-25 13:56:19,202][00699] Fps is (10 sec: 3275.1, 60 sec: 3617.8, 300 sec: 3582.2). Total num frames: 3747840. Throughput: 0: 915.0. Samples: 937784. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:56:19,210][00699] Avg episode reward: [(0, '18.018')] | |
| [2023-02-25 13:56:19,219][12789] Saving new best policy, reward=18.018! | |
| [2023-02-25 13:56:24,196][00699] Fps is (10 sec: 2868.1, 60 sec: 3686.4, 300 sec: 3596.2). Total num frames: 3764224. Throughput: 0: 913.3. Samples: 942154. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:56:24,203][00699] Avg episode reward: [(0, '19.681')] | |
| [2023-02-25 13:56:24,206][12789] Saving new best policy, reward=19.681! | |
| [2023-02-25 13:56:24,681][12803] Updated weights for policy 0, policy_version 920 (0.0013) | |
| [2023-02-25 13:56:29,196][00699] Fps is (10 sec: 4098.2, 60 sec: 3754.7, 300 sec: 3623.9). Total num frames: 3788800. Throughput: 0: 941.6. Samples: 945614. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:56:29,198][00699] Avg episode reward: [(0, '18.967')] | |
| [2023-02-25 13:56:33,342][12803] Updated weights for policy 0, policy_version 930 (0.0012) | |
| [2023-02-25 13:56:34,196][00699] Fps is (10 sec: 4915.2, 60 sec: 3822.9, 300 sec: 3637.8). Total num frames: 3813376. Throughput: 0: 948.5. Samples: 952522. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) | |
| [2023-02-25 13:56:34,203][00699] Avg episode reward: [(0, '18.027')] | |
| [2023-02-25 13:56:39,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3596.2). Total num frames: 3825664. Throughput: 0: 913.2. Samples: 957390. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) | |
| [2023-02-25 13:56:39,202][00699] Avg episode reward: [(0, '17.612')] | |
| [2023-02-25 13:56:44,196][00699] Fps is (10 sec: 2457.6, 60 sec: 3686.4, 300 sec: 3596.2). Total num frames: 3837952. Throughput: 0: 913.7. Samples: 959508. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:56:44,199][00699] Avg episode reward: [(0, '18.667')] | |
| [2023-02-25 13:56:46,003][12803] Updated weights for policy 0, policy_version 940 (0.0025) | |
| [2023-02-25 13:56:49,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3637.8). Total num frames: 3862528. Throughput: 0: 952.2. Samples: 965656. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) | |
| [2023-02-25 13:56:49,204][00699] Avg episode reward: [(0, '18.743')] | |
| [2023-02-25 13:56:54,196][00699] Fps is (10 sec: 4915.2, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 3887104. Throughput: 0: 956.5. Samples: 972618. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:56:54,204][00699] Avg episode reward: [(0, '20.027')] | |
| [2023-02-25 13:56:54,210][12789] Saving new best policy, reward=20.027! | |
| [2023-02-25 13:56:55,173][12803] Updated weights for policy 0, policy_version 950 (0.0019) | |
| [2023-02-25 13:56:59,196][00699] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3665.6). Total num frames: 3899392. Throughput: 0: 928.8. Samples: 974748. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:56:59,204][00699] Avg episode reward: [(0, '20.815')] | |
| [2023-02-25 13:56:59,215][12789] Saving new best policy, reward=20.815! | |
| [2023-02-25 13:57:04,196][00699] Fps is (10 sec: 2867.2, 60 sec: 3754.7, 300 sec: 3651.7). Total num frames: 3915776. Throughput: 0: 918.1. Samples: 979092. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:57:04,200][00699] Avg episode reward: [(0, '20.811')] | |
| [2023-02-25 13:57:07,565][12803] Updated weights for policy 0, policy_version 960 (0.0015) | |
| [2023-02-25 13:57:09,197][00699] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3679.5). Total num frames: 3936256. Throughput: 0: 960.0. Samples: 985352. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:57:09,204][00699] Avg episode reward: [(0, '20.494')] | |
| [2023-02-25 13:57:14,200][00699] Fps is (10 sec: 4504.2, 60 sec: 3754.7, 300 sec: 3693.3). Total num frames: 3960832. Throughput: 0: 959.1. Samples: 988776. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) | |
| [2023-02-25 13:57:14,203][00699] Avg episode reward: [(0, '18.956')] | |
| [2023-02-25 13:57:17,451][12803] Updated weights for policy 0, policy_version 970 (0.0011) | |
| [2023-02-25 13:57:19,197][00699] Fps is (10 sec: 4096.0, 60 sec: 3823.3, 300 sec: 3679.5). Total num frames: 3977216. Throughput: 0: 929.5. Samples: 994348. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) | |
| [2023-02-25 13:57:19,203][00699] Avg episode reward: [(0, '19.121')] | |
| [2023-02-25 13:57:24,196][00699] Fps is (10 sec: 2868.1, 60 sec: 3754.7, 300 sec: 3651.7). Total num frames: 3989504. Throughput: 0: 919.7. Samples: 998778. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) | |
| [2023-02-25 13:57:24,201][00699] Avg episode reward: [(0, '18.999')] | |
| [2023-02-25 13:57:27,520][12789] Stopping Batcher_0... | |
| [2023-02-25 13:57:27,521][12789] Loop batcher_evt_loop terminating... | |
| [2023-02-25 13:57:27,521][00699] Component Batcher_0 stopped! | |
| [2023-02-25 13:57:27,532][12789] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... | |
| [2023-02-25 13:57:27,583][12809] Stopping RolloutWorker_w4... | |
| [2023-02-25 13:57:27,586][12808] Stopping RolloutWorker_w2... | |
| [2023-02-25 13:57:27,586][12808] Loop rollout_proc2_evt_loop terminating... | |
| [2023-02-25 13:57:27,583][00699] Component RolloutWorker_w4 stopped! | |
| [2023-02-25 13:57:27,589][00699] Component RolloutWorker_w2 stopped! | |
| [2023-02-25 13:57:27,597][12814] Stopping RolloutWorker_w6... | |
| [2023-02-25 13:57:27,597][00699] Component RolloutWorker_w6 stopped! | |
| [2023-02-25 13:57:27,583][12809] Loop rollout_proc4_evt_loop terminating... | |
| [2023-02-25 13:57:27,603][12803] Weights refcount: 2 0 | |
| [2023-02-25 13:57:27,600][12814] Loop rollout_proc6_evt_loop terminating... | |
| [2023-02-25 13:57:27,609][00699] Component InferenceWorker_p0-w0 stopped! | |
| [2023-02-25 13:57:27,609][12803] Stopping InferenceWorker_p0-w0... | |
| [2023-02-25 13:57:27,612][12803] Loop inference_proc0-0_evt_loop terminating... | |
| [2023-02-25 13:57:27,627][12805] Stopping RolloutWorker_w0... | |
| [2023-02-25 13:57:27,631][12805] Loop rollout_proc0_evt_loop terminating... | |
| [2023-02-25 13:57:27,626][00699] Component RolloutWorker_w1 stopped! | |
| [2023-02-25 13:57:27,634][00699] Component RolloutWorker_w0 stopped! | |
| [2023-02-25 13:57:27,626][12804] Stopping RolloutWorker_w1... | |
| [2023-02-25 13:57:27,640][12804] Loop rollout_proc1_evt_loop terminating... | |
| [2023-02-25 13:57:27,642][00699] Component RolloutWorker_w3 stopped! | |
| [2023-02-25 13:57:27,645][12813] Stopping RolloutWorker_w3... | |
| [2023-02-25 13:57:27,652][00699] Component RolloutWorker_w7 stopped! | |
| [2023-02-25 13:57:27,657][12822] Stopping RolloutWorker_w7... | |
| [2023-02-25 13:57:27,649][12813] Loop rollout_proc3_evt_loop terminating... | |
| [2023-02-25 13:57:27,660][12822] Loop rollout_proc7_evt_loop terminating... | |
| [2023-02-25 13:57:27,666][00699] Component RolloutWorker_w5 stopped! | |
| [2023-02-25 13:57:27,671][12819] Stopping RolloutWorker_w5... | |
| [2023-02-25 13:57:27,672][12819] Loop rollout_proc5_evt_loop terminating... | |
| [2023-02-25 13:57:27,728][12789] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000783_3207168.pth | |
| [2023-02-25 13:57:27,742][12789] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... | |
| [2023-02-25 13:57:27,930][00699] Component LearnerWorker_p0 stopped! | |
| [2023-02-25 13:57:27,930][12789] Stopping LearnerWorker_p0... | |
| [2023-02-25 13:57:27,937][12789] Loop learner_proc0_evt_loop terminating... | |
| [2023-02-25 13:57:27,937][00699] Waiting for process learner_proc0 to stop... | |
| [2023-02-25 13:57:29,680][00699] Waiting for process inference_proc0-0 to join... | |
| [2023-02-25 13:57:29,835][00699] Waiting for process rollout_proc0 to join... | |
| [2023-02-25 13:57:30,434][00699] Waiting for process rollout_proc1 to join... | |
| [2023-02-25 13:57:30,436][00699] Waiting for process rollout_proc2 to join... | |
| [2023-02-25 13:57:30,440][00699] Waiting for process rollout_proc3 to join... | |
| [2023-02-25 13:57:30,444][00699] Waiting for process rollout_proc4 to join... | |
| [2023-02-25 13:57:30,446][00699] Waiting for process rollout_proc5 to join... | |
| [2023-02-25 13:57:30,448][00699] Waiting for process rollout_proc6 to join... | |
| [2023-02-25 13:57:30,450][00699] Waiting for process rollout_proc7 to join... | |
| [2023-02-25 13:57:30,451][00699] Batcher 0 profile tree view: | |
| batching: 26.9716, releasing_batches: 0.0232 | |
| [2023-02-25 13:57:30,452][00699] InferenceWorker_p0-w0 profile tree view: | |
| wait_policy: 0.0000 | |
| wait_policy_total: 551.9726 | |
| update_model: 7.9287 | |
| weight_update: 0.0039 | |
| one_step: 0.0123 | |
| handle_policy_step: 553.2146 | |
| deserialize: 15.3083, stack: 3.0778, obs_to_device_normalize: 118.8941, forward: 270.8024, send_messages: 27.3986 | |
| prepare_outputs: 89.8224 | |
| to_cpu: 56.1061 | |
| [2023-02-25 13:57:30,454][00699] Learner 0 profile tree view: | |
| misc: 0.0065, prepare_batch: 16.5891 | |
| train: 77.8750 | |
| epoch_init: 0.0121, minibatch_init: 0.0073, losses_postprocess: 0.5452, kl_divergence: 0.6278, after_optimizer: 32.9051 | |
| calculate_losses: 28.1156 | |
| losses_init: 0.0036, forward_head: 1.8022, bptt_initial: 18.4407, tail: 1.2613, advantages_returns: 0.3129, losses: 3.5919 | |
| bptt: 2.2877 | |
| bptt_forward_core: 2.2119 | |
| update: 14.9934 | |
| clip: 1.4682 | |
| [2023-02-25 13:57:30,455][00699] RolloutWorker_w0 profile tree view: | |
| wait_for_trajectories: 0.3872, enqueue_policy_requests: 151.8462, env_step: 868.9891, overhead: 23.0898, complete_rollouts: 6.7247 | |
| save_policy_outputs: 21.5644 | |
| split_output_tensors: 10.6949 | |
| [2023-02-25 13:57:30,457][00699] RolloutWorker_w7 profile tree view: | |
| wait_for_trajectories: 0.3382, enqueue_policy_requests: 157.7623, env_step: 863.9580, overhead: 23.0078, complete_rollouts: 8.1152 | |
| save_policy_outputs: 21.0963 | |
| split_output_tensors: 10.0864 | |
| [2023-02-25 13:57:30,458][00699] Loop Runner_EvtLoop terminating... | |
| [2023-02-25 13:57:30,460][00699] Runner profile tree view: | |
| main_loop: 1179.6343 | |
| [2023-02-25 13:57:30,461][00699] Collected {0: 4005888}, FPS: 3395.9 | |
| [2023-02-25 13:57:30,516][00699] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json | |
| [2023-02-25 13:57:30,518][00699] Overriding arg 'num_workers' with value 1 passed from command line | |
| [2023-02-25 13:57:30,520][00699] Adding new argument 'no_render'=True that is not in the saved config file! | |
| [2023-02-25 13:57:30,521][00699] Adding new argument 'save_video'=True that is not in the saved config file! | |
| [2023-02-25 13:57:30,523][00699] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! | |
| [2023-02-25 13:57:30,527][00699] Adding new argument 'video_name'=None that is not in the saved config file! | |
| [2023-02-25 13:57:30,530][00699] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file! | |
| [2023-02-25 13:57:30,531][00699] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! | |
| [2023-02-25 13:57:30,533][00699] Adding new argument 'push_to_hub'=False that is not in the saved config file! | |
| [2023-02-25 13:57:30,536][00699] Adding new argument 'hf_repository'=None that is not in the saved config file! | |
| [2023-02-25 13:57:30,538][00699] Adding new argument 'policy_index'=0 that is not in the saved config file! | |
| [2023-02-25 13:57:30,540][00699] Adding new argument 'eval_deterministic'=False that is not in the saved config file! | |
| [2023-02-25 13:57:30,542][00699] Adding new argument 'train_script'=None that is not in the saved config file! | |
| [2023-02-25 13:57:30,544][00699] Adding new argument 'enjoy_script'=None that is not in the saved config file! | |
| [2023-02-25 13:57:30,546][00699] Using frameskip 1 and render_action_repeat=4 for evaluation | |
| [2023-02-25 13:57:30,566][00699] RunningMeanStd input shape: (3, 72, 128) | |
| [2023-02-25 13:57:30,567][00699] RunningMeanStd input shape: (1,) | |
| [2023-02-25 13:57:30,584][00699] ConvEncoder: input_channels=3 | |
| [2023-02-25 13:57:30,623][00699] Conv encoder output size: 512 | |
| [2023-02-25 13:57:30,625][00699] Policy head output size: 512 | |
| [2023-02-25 13:57:30,649][00699] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... | |
| [2023-02-25 13:57:32,046][00699] Num frames 100... | |
| [2023-02-25 13:57:32,155][00699] Num frames 200... | |
| [2023-02-25 13:57:32,265][00699] Num frames 300... | |
| [2023-02-25 13:57:32,354][00699] Avg episode rewards: #0: 6.300, true rewards: #0: 3.300 | |
| [2023-02-25 13:57:32,358][00699] Avg episode reward: 6.300, avg true_objective: 3.300 | |
| [2023-02-25 13:57:32,439][00699] Num frames 400... | |
| [2023-02-25 13:57:32,555][00699] Num frames 500... | |
| [2023-02-25 13:57:32,669][00699] Num frames 600... | |
| [2023-02-25 13:57:32,782][00699] Num frames 700... | |
| [2023-02-25 13:57:32,898][00699] Num frames 800... | |
| [2023-02-25 13:57:33,027][00699] Num frames 900... | |
| [2023-02-25 13:57:33,141][00699] Num frames 1000... | |
| [2023-02-25 13:57:33,308][00699] Avg episode rewards: #0: 9.490, true rewards: #0: 5.490 | |
| [2023-02-25 13:57:33,310][00699] Avg episode reward: 9.490, avg true_objective: 5.490 | |
| [2023-02-25 13:57:33,316][00699] Num frames 1100... | |
| [2023-02-25 13:57:33,433][00699] Num frames 1200... | |
| [2023-02-25 13:57:33,545][00699] Num frames 1300... | |
| [2023-02-25 13:57:33,662][00699] Num frames 1400... | |
| [2023-02-25 13:57:33,784][00699] Num frames 1500... | |
| [2023-02-25 13:57:33,857][00699] Avg episode rewards: #0: 8.380, true rewards: #0: 5.047 | |
| [2023-02-25 13:57:33,858][00699] Avg episode reward: 8.380, avg true_objective: 5.047 | |
| [2023-02-25 13:57:33,957][00699] Num frames 1600... | |
| [2023-02-25 13:57:34,076][00699] Num frames 1700... | |
| [2023-02-25 13:57:34,194][00699] Num frames 1800... | |
| [2023-02-25 13:57:34,308][00699] Num frames 1900... | |
| [2023-02-25 13:57:34,427][00699] Num frames 2000... | |
| [2023-02-25 13:57:34,539][00699] Num frames 2100... | |
| [2023-02-25 13:57:34,652][00699] Num frames 2200... | |
| [2023-02-25 13:57:34,765][00699] Avg episode rewards: #0: 10.375, true rewards: #0: 5.625 | |
| [2023-02-25 13:57:34,767][00699] Avg episode reward: 10.375, avg true_objective: 5.625 | |
| [2023-02-25 13:57:34,828][00699] Num frames 2300... | |
| [2023-02-25 13:57:34,948][00699] Num frames 2400... | |
| [2023-02-25 13:57:35,076][00699] Num frames 2500... | |
| [2023-02-25 13:57:35,190][00699] Num frames 2600... | |
| [2023-02-25 13:57:35,347][00699] Num frames 2700... | |
| [2023-02-25 13:57:35,511][00699] Num frames 2800... | |
| [2023-02-25 13:57:35,668][00699] Num frames 2900... | |
| [2023-02-25 13:57:35,820][00699] Num frames 3000... | |
| [2023-02-25 13:57:35,972][00699] Num frames 3100... | |
| [2023-02-25 13:57:36,136][00699] Num frames 3200... | |
| [2023-02-25 13:57:36,295][00699] Num frames 3300... | |
| [2023-02-25 13:57:36,453][00699] Num frames 3400... | |
| [2023-02-25 13:57:36,625][00699] Avg episode rewards: #0: 14.732, true rewards: #0: 6.932 | |
| [2023-02-25 13:57:36,631][00699] Avg episode reward: 14.732, avg true_objective: 6.932 | |
| [2023-02-25 13:57:36,698][00699] Num frames 3500... | |
| [2023-02-25 13:57:36,866][00699] Num frames 3600... | |
| [2023-02-25 13:57:37,029][00699] Num frames 3700... | |
| [2023-02-25 13:57:37,204][00699] Num frames 3800... | |
| [2023-02-25 13:57:37,361][00699] Num frames 3900... | |
| [2023-02-25 13:57:37,532][00699] Num frames 4000... | |
| [2023-02-25 13:57:37,691][00699] Num frames 4100... | |
| [2023-02-25 13:57:37,850][00699] Num frames 4200... | |
| [2023-02-25 13:57:38,007][00699] Num frames 4300... | |
| [2023-02-25 13:57:38,175][00699] Num frames 4400... | |
| [2023-02-25 13:57:38,333][00699] Num frames 4500... | |
| [2023-02-25 13:57:38,495][00699] Num frames 4600... | |
| [2023-02-25 13:57:38,654][00699] Num frames 4700... | |
| [2023-02-25 13:57:38,784][00699] Num frames 4800... | |
| [2023-02-25 13:57:38,896][00699] Num frames 4900... | |
| [2023-02-25 13:57:39,014][00699] Num frames 5000... | |
| [2023-02-25 13:57:39,126][00699] Num frames 5100... | |
| [2023-02-25 13:57:39,247][00699] Num frames 5200... | |
| [2023-02-25 13:57:39,368][00699] Num frames 5300... | |
| [2023-02-25 13:57:39,448][00699] Avg episode rewards: #0: 19.703, true rewards: #0: 8.870 | |
| [2023-02-25 13:57:39,451][00699] Avg episode reward: 19.703, avg true_objective: 8.870 | |
| [2023-02-25 13:57:39,549][00699] Num frames 5400... | |
| [2023-02-25 13:57:39,661][00699] Num frames 5500... | |
| [2023-02-25 13:57:39,774][00699] Num frames 5600... | |
| [2023-02-25 13:57:39,888][00699] Num frames 5700... | |
| [2023-02-25 13:57:39,999][00699] Num frames 5800... | |
| [2023-02-25 13:57:40,111][00699] Num frames 5900... | |
| [2023-02-25 13:57:40,230][00699] Num frames 6000... | |
| [2023-02-25 13:57:40,343][00699] Num frames 6100... | |
| [2023-02-25 13:57:40,488][00699] Avg episode rewards: #0: 19.257, true rewards: #0: 8.829 | |
| [2023-02-25 13:57:40,489][00699] Avg episode reward: 19.257, avg true_objective: 8.829 | |
| [2023-02-25 13:57:40,515][00699] Num frames 6200... | |
| [2023-02-25 13:57:40,630][00699] Num frames 6300... | |
| [2023-02-25 13:57:40,746][00699] Num frames 6400... | |
| [2023-02-25 13:57:40,860][00699] Num frames 6500... | |
| [2023-02-25 13:57:40,983][00699] Num frames 6600... | |
| [2023-02-25 13:57:41,075][00699] Avg episode rewards: #0: 17.660, true rewards: #0: 8.285 | |
| [2023-02-25 13:57:41,077][00699] Avg episode reward: 17.660, avg true_objective: 8.285 | |
| [2023-02-25 13:57:41,173][00699] Num frames 6700... | |
| [2023-02-25 13:57:41,290][00699] Num frames 6800... | |
| [2023-02-25 13:57:41,408][00699] Num frames 6900... | |
| [2023-02-25 13:57:41,516][00699] Num frames 7000... | |
| [2023-02-25 13:57:41,627][00699] Num frames 7100... | |
| [2023-02-25 13:57:41,734][00699] Num frames 7200... | |
| [2023-02-25 13:57:41,842][00699] Num frames 7300... | |
| [2023-02-25 13:57:41,953][00699] Num frames 7400... | |
| [2023-02-25 13:57:42,062][00699] Num frames 7500... | |
| [2023-02-25 13:57:42,187][00699] Avg episode rewards: #0: 17.951, true rewards: #0: 8.396 | |
| [2023-02-25 13:57:42,189][00699] Avg episode reward: 17.951, avg true_objective: 8.396 | |
| [2023-02-25 13:57:42,251][00699] Num frames 7600... | |
| [2023-02-25 13:57:42,364][00699] Num frames 7700... | |
| [2023-02-25 13:57:42,479][00699] Num frames 7800... | |
| [2023-02-25 13:57:42,590][00699] Num frames 7900... | |
| [2023-02-25 13:57:42,701][00699] Num frames 8000... | |
| [2023-02-25 13:57:42,813][00699] Num frames 8100... | |
| [2023-02-25 13:57:42,905][00699] Avg episode rewards: #0: 17.032, true rewards: #0: 8.132 | |
| [2023-02-25 13:57:42,907][00699] Avg episode reward: 17.032, avg true_objective: 8.132 | |
| [2023-02-25 13:58:33,047][00699] Replay video saved to /content/train_dir/default_experiment/replay.mp4! | |
| [2023-02-25 13:58:33,400][00699] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json | |
| [2023-02-25 13:58:33,403][00699] Overriding arg 'num_workers' with value 1 passed from command line | |
| [2023-02-25 13:58:33,407][00699] Adding new argument 'no_render'=True that is not in the saved config file! | |
| [2023-02-25 13:58:33,410][00699] Adding new argument 'save_video'=True that is not in the saved config file! | |
| [2023-02-25 13:58:33,413][00699] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! | |
| [2023-02-25 13:58:33,415][00699] Adding new argument 'video_name'=None that is not in the saved config file! | |
| [2023-02-25 13:58:33,418][00699] Adding new argument 'max_num_frames'=100000 that is not in the saved config file! | |
| [2023-02-25 13:58:33,420][00699] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! | |
| [2023-02-25 13:58:33,421][00699] Adding new argument 'push_to_hub'=True that is not in the saved config file! | |
| [2023-02-25 13:58:33,426][00699] Adding new argument 'hf_repository'='RegisGraptin/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file! | |
| [2023-02-25 13:58:33,429][00699] Adding new argument 'policy_index'=0 that is not in the saved config file! | |
| [2023-02-25 13:58:33,431][00699] Adding new argument 'eval_deterministic'=False that is not in the saved config file! | |
| [2023-02-25 13:58:33,434][00699] Adding new argument 'train_script'=None that is not in the saved config file! | |
| [2023-02-25 13:58:33,442][00699] Adding new argument 'enjoy_script'=None that is not in the saved config file! | |
| [2023-02-25 13:58:33,446][00699] Using frameskip 1 and render_action_repeat=4 for evaluation | |
| [2023-02-25 13:58:33,475][00699] RunningMeanStd input shape: (3, 72, 128) | |
| [2023-02-25 13:58:33,478][00699] RunningMeanStd input shape: (1,) | |
| [2023-02-25 13:58:33,505][00699] ConvEncoder: input_channels=3 | |
| [2023-02-25 13:58:33,570][00699] Conv encoder output size: 512 | |
| [2023-02-25 13:58:33,572][00699] Policy head output size: 512 | |
| [2023-02-25 13:58:33,614][00699] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... | |
| [2023-02-25 13:58:34,388][00699] Num frames 100... | |
| [2023-02-25 13:58:34,563][00699] Num frames 200... | |
| [2023-02-25 13:58:34,750][00699] Num frames 300... | |
| [2023-02-25 13:58:34,934][00699] Num frames 400... | |
| [2023-02-25 13:58:35,091][00699] Avg episode rewards: #0: 7.480, true rewards: #0: 4.480 | |
| [2023-02-25 13:58:35,093][00699] Avg episode reward: 7.480, avg true_objective: 4.480 | |
| [2023-02-25 13:58:35,202][00699] Num frames 500... | |
| [2023-02-25 13:58:35,390][00699] Num frames 600... | |
| [2023-02-25 13:58:35,569][00699] Num frames 700... | |
| [2023-02-25 13:58:35,752][00699] Num frames 800... | |
| [2023-02-25 13:58:35,934][00699] Num frames 900... | |
| [2023-02-25 13:58:36,112][00699] Num frames 1000... | |
| [2023-02-25 13:58:36,215][00699] Avg episode rewards: #0: 10.120, true rewards: #0: 5.120 | |
| [2023-02-25 13:58:36,218][00699] Avg episode reward: 10.120, avg true_objective: 5.120 | |
| [2023-02-25 13:58:36,367][00699] Num frames 1100... | |
| [2023-02-25 13:58:36,554][00699] Num frames 1200... | |
| [2023-02-25 13:58:36,737][00699] Num frames 1300... | |
| [2023-02-25 13:58:36,921][00699] Num frames 1400... | |
| [2023-02-25 13:58:37,104][00699] Num frames 1500... | |
| [2023-02-25 13:58:37,281][00699] Num frames 1600... | |
| [2023-02-25 13:58:37,467][00699] Num frames 1700... | |
| [2023-02-25 13:58:37,645][00699] Num frames 1800... | |
| [2023-02-25 13:58:37,822][00699] Num frames 1900... | |
| [2023-02-25 13:58:38,001][00699] Num frames 2000... | |
| [2023-02-25 13:58:38,169][00699] Num frames 2100... | |
| [2023-02-25 13:58:38,360][00699] Avg episode rewards: #0: 15.920, true rewards: #0: 7.253 | |
| [2023-02-25 13:58:38,362][00699] Avg episode reward: 15.920, avg true_objective: 7.253 | |
| [2023-02-25 13:58:38,415][00699] Num frames 2200... | |
| [2023-02-25 13:58:38,599][00699] Num frames 2300... | |
| [2023-02-25 13:58:38,788][00699] Num frames 2400... | |
| [2023-02-25 13:58:38,977][00699] Num frames 2500... | |
| [2023-02-25 13:58:39,168][00699] Num frames 2600... | |
| [2023-02-25 13:58:39,348][00699] Num frames 2700... | |
| [2023-02-25 13:58:39,532][00699] Num frames 2800... | |
| [2023-02-25 13:58:39,725][00699] Num frames 2900... | |
| [2023-02-25 13:58:39,922][00699] Num frames 3000... | |
| [2023-02-25 13:58:40,124][00699] Num frames 3100... | |
| [2023-02-25 13:58:40,302][00699] Num frames 3200... | |
| [2023-02-25 13:58:40,476][00699] Num frames 3300... | |
| [2023-02-25 13:58:40,664][00699] Num frames 3400... | |
| [2023-02-25 13:58:40,883][00699] Avg episode rewards: #0: 20.220, true rewards: #0: 8.720 | |
| [2023-02-25 13:58:40,884][00699] Avg episode reward: 20.220, avg true_objective: 8.720 | |
| [2023-02-25 13:58:40,912][00699] Num frames 3500... | |
| [2023-02-25 13:58:41,104][00699] Num frames 3600... | |
| [2023-02-25 13:58:41,299][00699] Num frames 3700... | |
| [2023-02-25 13:58:41,501][00699] Num frames 3800... | |
| [2023-02-25 13:58:41,675][00699] Num frames 3900... | |
| [2023-02-25 13:58:41,833][00699] Num frames 4000... | |
| [2023-02-25 13:58:41,985][00699] Num frames 4100... | |
| [2023-02-25 13:58:42,125][00699] Num frames 4200... | |
| [2023-02-25 13:58:42,238][00699] Num frames 4300... | |
| [2023-02-25 13:58:42,355][00699] Num frames 4400... | |
| [2023-02-25 13:58:42,414][00699] Avg episode rewards: #0: 20.002, true rewards: #0: 8.802 | |
| [2023-02-25 13:58:42,416][00699] Avg episode reward: 20.002, avg true_objective: 8.802 | |
| [2023-02-25 13:58:42,531][00699] Num frames 4500... | |
| [2023-02-25 13:58:42,658][00699] Num frames 4600... | |
| [2023-02-25 13:58:42,772][00699] Num frames 4700... | |
| [2023-02-25 13:58:42,883][00699] Num frames 4800... | |
| [2023-02-25 13:58:42,959][00699] Avg episode rewards: #0: 17.528, true rewards: #0: 8.028 | |
| [2023-02-25 13:58:42,960][00699] Avg episode reward: 17.528, avg true_objective: 8.028 | |
| [2023-02-25 13:58:43,059][00699] Num frames 4900... | |
| [2023-02-25 13:58:43,174][00699] Num frames 5000... | |
| [2023-02-25 13:58:43,284][00699] Num frames 5100... | |
| [2023-02-25 13:58:43,396][00699] Num frames 5200... | |
| [2023-02-25 13:58:43,506][00699] Num frames 5300... | |
| [2023-02-25 13:58:43,616][00699] Num frames 5400... | |
| [2023-02-25 13:58:43,726][00699] Num frames 5500... | |
| [2023-02-25 13:58:43,834][00699] Num frames 5600... | |
| [2023-02-25 13:58:43,944][00699] Num frames 5700... | |
| [2023-02-25 13:58:44,015][00699] Avg episode rewards: #0: 17.590, true rewards: #0: 8.161 | |
| [2023-02-25 13:58:44,017][00699] Avg episode reward: 17.590, avg true_objective: 8.161 | |
| [2023-02-25 13:58:44,128][00699] Num frames 5800... | |
| [2023-02-25 13:58:44,241][00699] Num frames 5900... | |
| [2023-02-25 13:58:44,351][00699] Num frames 6000... | |
| [2023-02-25 13:58:44,463][00699] Num frames 6100... | |
| [2023-02-25 13:58:44,574][00699] Num frames 6200... | |
| [2023-02-25 13:58:44,659][00699] Avg episode rewards: #0: 16.406, true rewards: #0: 7.781 | |
| [2023-02-25 13:58:44,661][00699] Avg episode reward: 16.406, avg true_objective: 7.781 | |
| [2023-02-25 13:58:44,750][00699] Num frames 6300... | |
| [2023-02-25 13:58:44,871][00699] Num frames 6400... | |
| [2023-02-25 13:58:44,986][00699] Num frames 6500... | |
| [2023-02-25 13:58:45,106][00699] Num frames 6600... | |
| [2023-02-25 13:58:45,222][00699] Num frames 6700... | |
| [2023-02-25 13:58:45,332][00699] Num frames 6800... | |
| [2023-02-25 13:58:45,442][00699] Num frames 6900... | |
| [2023-02-25 13:58:45,554][00699] Num frames 7000... | |
| [2023-02-25 13:58:45,667][00699] Num frames 7100... | |
| [2023-02-25 13:58:45,777][00699] Num frames 7200... | |
| [2023-02-25 13:58:45,889][00699] Num frames 7300... | |
| [2023-02-25 13:58:46,000][00699] Num frames 7400... | |
| [2023-02-25 13:58:46,070][00699] Avg episode rewards: #0: 17.232, true rewards: #0: 8.232 | |
| [2023-02-25 13:58:46,072][00699] Avg episode reward: 17.232, avg true_objective: 8.232 | |
| [2023-02-25 13:58:46,185][00699] Num frames 7500... | |
| [2023-02-25 13:58:46,300][00699] Num frames 7600... | |
| [2023-02-25 13:58:46,407][00699] Num frames 7700... | |
| [2023-02-25 13:58:46,517][00699] Num frames 7800... | |
| [2023-02-25 13:58:46,628][00699] Num frames 7900... | |
| [2023-02-25 13:58:46,742][00699] Num frames 8000... | |
| [2023-02-25 13:58:46,854][00699] Num frames 8100... | |
| [2023-02-25 13:58:46,956][00699] Avg episode rewards: #0: 17.043, true rewards: #0: 8.143 | |
| [2023-02-25 13:58:46,957][00699] Avg episode reward: 17.043, avg true_objective: 8.143 | |
| [2023-02-25 13:59:37,297][00699] Replay video saved to /content/train_dir/default_experiment/replay.mp4! | |