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rl_course_vizdoom_health_gathering_supreme

Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
Eval Results
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  7. config.json +142 -0
  8. replay.mp4 +3 -0
  9. sf_log.txt +734 -0
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+ ---
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+ library_name: sample-factory
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+ tags:
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - sample-factory
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+ model-index:
8
+ - name: APPO
9
+ results:
10
+ - task:
11
+ type: reinforcement-learning
12
+ name: reinforcement-learning
13
+ dataset:
14
+ name: doom_health_gathering_supreme
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+ type: doom_health_gathering_supreme
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+ metrics:
17
+ - type: mean_reward
18
+ value: 7.46 +/- 4.98
19
+ name: mean_reward
20
+ verified: false
21
+ ---
22
+
23
+ A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
24
+
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+ This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
26
+ Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
27
+
28
+
29
+ ## Downloading the model
30
+
31
+ After installing Sample-Factory, download the model with:
32
+ ```
33
+ python -m sample_factory.huggingface.load_from_hub -r Unterwexi/rl_course_vizdoom_health_gathering_supreme
34
+ ```
35
+
36
+
37
+ ## Using the model
38
+
39
+ To run the model after download, use the `enjoy` script corresponding to this environment:
40
+ ```
41
+ python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
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+ ```
43
+
44
+
45
+ You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
46
+ See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
47
+
48
+ ## Training with this model
49
+
50
+ To continue training with this model, use the `train` script corresponding to this environment:
51
+ ```
52
+ python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
53
+ ```
54
+
55
+ Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
56
+
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+ {
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+ "help": false,
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+ "algo": "APPO",
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+ "env": "doom_health_gathering_supreme",
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+ "experiment": "default_experiment",
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+ "train_dir": "/content/train_dir",
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+ "restart_behavior": "resume",
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+ "device": "gpu",
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+ "seed": null,
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+ "max_policy_lag": 1000,
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+ "num_workers": 8,
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+ "num_envs_per_worker": 4,
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+ "batch_size": 1024,
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+ "num_batches_per_epoch": 1,
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+ "num_epochs": 1,
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+ "rollout": 32,
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+ "recurrence": 32,
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+ "shuffle_minibatches": false,
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+ "gamma": 0.99,
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+ "reward_scale": 1.0,
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+ "reward_clip": 1000.0,
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+ "value_bootstrap": false,
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+ "normalize_returns": true,
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+ "exploration_loss_coeff": 0.001,
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+ "value_loss_coeff": 0.5,
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+ "kl_loss_coeff": 0.0,
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+ "exploration_loss": "symmetric_kl",
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+ "gae_lambda": 0.95,
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+ "ppo_clip_ratio": 0.1,
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+ "ppo_clip_value": 0.2,
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+ "with_vtrace": false,
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+ "vtrace_rho": 1.0,
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+ "vtrace_c": 1.0,
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+ "optimizer": "adam",
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+ "adam_eps": 1e-06,
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+ "adam_beta1": 0.9,
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+ "adam_beta2": 0.999,
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+ "max_grad_norm": 4.0,
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+ "learning_rate": 0.0001,
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+ "lr_schedule": "constant",
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+ "lr_schedule_kl_threshold": 0.008,
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+ "lr_adaptive_min": 1e-06,
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+ "lr_adaptive_max": 0.01,
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+ "obs_subtract_mean": 0.0,
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+ "obs_scale": 255.0,
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+ "normalize_input": true,
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+ "normalize_input_keys": null,
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+ "decorrelate_experience_max_seconds": 0,
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+ "decorrelate_envs_on_one_worker": true,
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+ "actor_worker_gpus": [],
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+ "set_workers_cpu_affinity": true,
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+ "force_envs_single_thread": false,
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+ "default_niceness": 0,
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+ "log_to_file": true,
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+ "experiment_summaries_interval": 10,
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+ "flush_summaries_interval": 30,
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+ "stats_avg": 100,
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+ "summaries_use_frameskip": true,
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+ "heartbeat_interval": 20,
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+ "heartbeat_reporting_interval": 600,
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+ "train_for_env_steps": 4000000,
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+ "train_for_seconds": 10000000000,
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+ "save_every_sec": 120,
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+ "keep_checkpoints": 2,
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+ "load_checkpoint_kind": "latest",
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+ "save_milestones_sec": -1,
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+ "save_best_every_sec": 5,
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+ "save_best_metric": "reward",
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+ "save_best_after": 100000,
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+ "benchmark": false,
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+ "encoder_mlp_layers": [
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+ 512,
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+ 512
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+ ],
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+ "encoder_conv_architecture": "convnet_simple",
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+ "encoder_conv_mlp_layers": [
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+ 512
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+ "use_rnn": true,
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+ "rnn_size": 512,
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+ "rnn_type": "gru",
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+ "rnn_num_layers": 1,
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+ "decoder_mlp_layers": [],
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+ "nonlinearity": "elu",
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+ "policy_initialization": "orthogonal",
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+ "policy_init_gain": 1.0,
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+ "wandb_job_type": "SF",
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+ "wandb_tags": [],
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+ "with_pbt": false,
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+ "pbt_mix_policies_in_one_env": true,
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+ "pbt_period_env_steps": 5000000,
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+ "pbt_start_mutation": 20000000,
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+ "pbt_replace_fraction": 0.3,
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+ "pbt_replace_reward_gap": 0.1,
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+ "pbt_replace_reward_gap_absolute": 1e-06,
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+ "pbt_optimize_gamma": false,
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+ "pbt_target_objective": "true_objective",
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+ "pbt_perturb_min": 1.1,
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+ "pbt_perturb_max": 1.5,
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+ "num_agents": -1,
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+ "num_humans": 0,
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+ "start_bot_difficulty": null,
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+ "res_h": 72,
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+ "wide_aspect_ratio": false,
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+ "eval_env_frameskip": 1,
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+ "fps": 35,
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+ "command_line": "--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000",
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+ "cli_args": {
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+ "env": "doom_health_gathering_supreme",
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+ "num_workers": 8,
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+ "num_envs_per_worker": 4,
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+ "train_for_env_steps": 4000000
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+ },
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+ "git_hash": "unknown",
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+ "git_repo_name": "not a git repository"
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+ }
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1
+ [2023-02-22 15:55:46,126][11727] Saving configuration to /content/train_dir/default_experiment/config.json...
2
+ [2023-02-22 15:55:46,128][11727] Rollout worker 0 uses device cpu
3
+ [2023-02-22 15:55:46,129][11727] Rollout worker 1 uses device cpu
4
+ [2023-02-22 15:55:46,130][11727] Rollout worker 2 uses device cpu
5
+ [2023-02-22 15:55:46,132][11727] Rollout worker 3 uses device cpu
6
+ [2023-02-22 15:55:46,133][11727] Rollout worker 4 uses device cpu
7
+ [2023-02-22 15:55:46,136][11727] Rollout worker 5 uses device cpu
8
+ [2023-02-22 15:55:46,137][11727] Rollout worker 6 uses device cpu
9
+ [2023-02-22 15:55:46,139][11727] Rollout worker 7 uses device cpu
10
+ [2023-02-22 15:55:46,236][11727] Using GPUs [0] for process 0 (actually maps to GPUs [0])
11
+ [2023-02-22 15:55:46,238][11727] InferenceWorker_p0-w0: min num requests: 2
12
+ [2023-02-22 15:55:46,268][11727] Starting all processes...
13
+ [2023-02-22 15:55:46,270][11727] Starting process learner_proc0
14
+ [2023-02-22 15:55:46,326][11727] Starting all processes...
15
+ [2023-02-22 15:55:46,338][11727] Starting process inference_proc0-0
16
+ [2023-02-22 15:55:46,338][11727] Starting process rollout_proc0
17
+ [2023-02-22 15:55:46,339][11727] Starting process rollout_proc1
18
+ [2023-02-22 15:55:46,340][11727] Starting process rollout_proc2
19
+ [2023-02-22 15:55:46,342][11727] Starting process rollout_proc3
20
+ [2023-02-22 15:55:46,342][11727] Starting process rollout_proc4
21
+ [2023-02-22 15:55:46,357][11727] Starting process rollout_proc5
22
+ [2023-02-22 15:55:46,358][11727] Starting process rollout_proc6
23
+ [2023-02-22 15:55:46,358][11727] Starting process rollout_proc7
24
+ [2023-02-22 15:55:48,129][11948] Using GPUs [0] for process 0 (actually maps to GPUs [0])
25
+ [2023-02-22 15:55:48,129][11948] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
26
+ [2023-02-22 15:55:48,448][11949] Worker 0 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
27
+ [2023-02-22 15:55:48,552][11934] Using GPUs [0] for process 0 (actually maps to GPUs [0])
28
+ [2023-02-22 15:55:48,553][11934] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
29
+ [2023-02-22 15:55:48,778][11974] Worker 6 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
30
+ [2023-02-22 15:55:48,778][11953] Worker 3 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
31
+ [2023-02-22 15:55:48,807][11950] Worker 1 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
32
+ [2023-02-22 15:55:48,860][11951] Worker 2 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
33
+ [2023-02-22 15:55:48,864][11973] Worker 7 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
34
+ [2023-02-22 15:55:48,895][11975] Worker 4 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
35
+ [2023-02-22 15:55:48,947][11970] Worker 5 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
36
+ [2023-02-22 15:55:49,003][11948] Num visible devices: 1
37
+ [2023-02-22 15:55:49,003][11934] Num visible devices: 1
38
+ [2023-02-22 15:55:49,028][11934] Starting seed is not provided
39
+ [2023-02-22 15:55:49,028][11934] Using GPUs [0] for process 0 (actually maps to GPUs [0])
40
+ [2023-02-22 15:55:49,028][11934] Initializing actor-critic model on device cuda:0
41
+ [2023-02-22 15:55:49,028][11934] RunningMeanStd input shape: (3, 72, 128)
42
+ [2023-02-22 15:55:49,030][11934] RunningMeanStd input shape: (1,)
43
+ [2023-02-22 15:55:49,044][11934] ConvEncoder: input_channels=3
44
+ [2023-02-22 15:55:49,304][11934] Conv encoder output size: 512
45
+ [2023-02-22 15:55:49,304][11934] Policy head output size: 512
46
+ [2023-02-22 15:55:49,345][11934] Created Actor Critic model with architecture:
47
+ [2023-02-22 15:55:49,345][11934] ActorCriticSharedWeights(
48
+ (obs_normalizer): ObservationNormalizer(
49
+ (running_mean_std): RunningMeanStdDictInPlace(
50
+ (running_mean_std): ModuleDict(
51
+ (obs): RunningMeanStdInPlace()
52
+ )
53
+ )
54
+ )
55
+ (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
56
+ (encoder): VizdoomEncoder(
57
+ (basic_encoder): ConvEncoder(
58
+ (enc): RecursiveScriptModule(
59
+ original_name=ConvEncoderImpl
60
+ (conv_head): RecursiveScriptModule(
61
+ original_name=Sequential
62
+ (0): RecursiveScriptModule(original_name=Conv2d)
63
+ (1): RecursiveScriptModule(original_name=ELU)
64
+ (2): RecursiveScriptModule(original_name=Conv2d)
65
+ (3): RecursiveScriptModule(original_name=ELU)
66
+ (4): RecursiveScriptModule(original_name=Conv2d)
67
+ (5): RecursiveScriptModule(original_name=ELU)
68
+ )
69
+ (mlp_layers): RecursiveScriptModule(
70
+ original_name=Sequential
71
+ (0): RecursiveScriptModule(original_name=Linear)
72
+ (1): RecursiveScriptModule(original_name=ELU)
73
+ )
74
+ )
75
+ )
76
+ )
77
+ (core): ModelCoreRNN(
78
+ (core): GRU(512, 512)
79
+ )
80
+ (decoder): MlpDecoder(
81
+ (mlp): Identity()
82
+ )
83
+ (critic_linear): Linear(in_features=512, out_features=1, bias=True)
84
+ (action_parameterization): ActionParameterizationDefault(
85
+ (distribution_linear): Linear(in_features=512, out_features=5, bias=True)
86
+ )
87
+ )
88
+ [2023-02-22 15:55:56,154][11934] Using optimizer <class 'torch.optim.adam.Adam'>
89
+ [2023-02-22 15:55:56,155][11934] No checkpoints found
90
+ [2023-02-22 15:55:56,156][11934] Did not load from checkpoint, starting from scratch!
91
+ [2023-02-22 15:55:56,156][11934] Initialized policy 0 weights for model version 0
92
+ [2023-02-22 15:55:56,158][11934] LearnerWorker_p0 finished initialization!
93
+ [2023-02-22 15:55:56,159][11934] Using GPUs [0] for process 0 (actually maps to GPUs [0])
94
+ [2023-02-22 15:55:56,267][11948] RunningMeanStd input shape: (3, 72, 128)
95
+ [2023-02-22 15:55:56,268][11948] RunningMeanStd input shape: (1,)
96
+ [2023-02-22 15:55:56,284][11948] ConvEncoder: input_channels=3
97
+ [2023-02-22 15:55:56,395][11948] Conv encoder output size: 512
98
+ [2023-02-22 15:55:56,396][11948] Policy head output size: 512
99
+ [2023-02-22 15:55:57,406][11727] 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)
100
+ [2023-02-22 15:55:59,174][11727] Inference worker 0-0 is ready!
101
+ [2023-02-22 15:55:59,176][11727] All inference workers are ready! Signal rollout workers to start!
102
+ [2023-02-22 15:55:59,195][11974] Doom resolution: 160x120, resize resolution: (128, 72)
103
+ [2023-02-22 15:55:59,195][11973] Doom resolution: 160x120, resize resolution: (128, 72)
104
+ [2023-02-22 15:55:59,201][11950] Doom resolution: 160x120, resize resolution: (128, 72)
105
+ [2023-02-22 15:55:59,202][11970] Doom resolution: 160x120, resize resolution: (128, 72)
106
+ [2023-02-22 15:55:59,202][11951] Doom resolution: 160x120, resize resolution: (128, 72)
107
+ [2023-02-22 15:55:59,202][11975] Doom resolution: 160x120, resize resolution: (128, 72)
108
+ [2023-02-22 15:55:59,202][11953] Doom resolution: 160x120, resize resolution: (128, 72)
109
+ [2023-02-22 15:55:59,202][11949] Doom resolution: 160x120, resize resolution: (128, 72)
110
+ [2023-02-22 15:55:59,257][11974] VizDoom game.init() threw an exception ViZDoomUnexpectedExitException('Controlled ViZDoom instance exited unexpectedly.'). Terminate process...
111
+ [2023-02-22 15:55:59,258][11974] EvtLoop [rollout_proc6_evt_loop, process=rollout_proc6] unhandled exception in slot='init' connected to emitter=Emitter(object_id='Sampler', signal_name='_inference_workers_initialized'), args=()
112
+ Traceback (most recent call last):
113
+ File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 228, in _game_init
114
+ self.game.init()
115
+ vizdoom.vizdoom.ViZDoomUnexpectedExitException: Controlled ViZDoom instance exited unexpectedly.
116
+
117
+ During handling of the above exception, another exception occurred:
118
+
119
+ Traceback (most recent call last):
120
+ File "/usr/local/lib/python3.8/dist-packages/signal_slot/signal_slot.py", line 355, in _process_signal
121
+ slot_callable(*args)
122
+ File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/rollout_worker.py", line 150, in init
123
+ env_runner.init(self.timing)
124
+ File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 418, in init
125
+ self._reset()
126
+ File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/sampling/non_batched_sampling.py", line 430, in _reset
127
+ observations, info = e.reset(seed=seed) # new way of doing seeding since Gym 0.26.0
128
+ File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset
129
+ return self.env.reset(**kwargs)
130
+ File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 125, in reset
131
+ obs, info = self.env.reset(**kwargs)
132
+ File "/usr/local/lib/python3.8/dist-packages/sample_factory/algo/utils/make_env.py", line 110, in reset
133
+ obs, info = self.env.reset(**kwargs)
134
+ File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/scenario_wrappers/gathering_reward_shaping.py", line 30, in reset
135
+ return self.env.reset(**kwargs)
136
+ File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 379, in reset
137
+ obs, info = self.env.reset(**kwargs)
138
+ File "/usr/local/lib/python3.8/dist-packages/sample_factory/envs/env_wrappers.py", line 84, in reset
139
+ obs, info = self.env.reset(**kwargs)
140
+ File "/usr/local/lib/python3.8/dist-packages/gym/core.py", line 323, in reset
141
+ return self.env.reset(**kwargs)
142
+ File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/wrappers/multiplayer_stats.py", line 51, in reset
143
+ return self.env.reset(**kwargs)
144
+ File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 323, in reset
145
+ self._ensure_initialized()
146
+ File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 274, in _ensure_initialized
147
+ self.initialize()
148
+ File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 269, in initialize
149
+ self._game_init()
150
+ File "/usr/local/lib/python3.8/dist-packages/sf_examples/vizdoom/doom/doom_gym.py", line 244, in _game_init
151
+ raise EnvCriticalError()
152
+ sample_factory.envs.env_utils.EnvCriticalError
153
+ [2023-02-22 15:55:59,260][11974] Unhandled exception in evt loop rollout_proc6_evt_loop
154
+ [2023-02-22 15:55:59,528][11973] Decorrelating experience for 0 frames...
155
+ [2023-02-22 15:55:59,528][11953] Decorrelating experience for 0 frames...
156
+ [2023-02-22 15:55:59,528][11951] Decorrelating experience for 0 frames...
157
+ [2023-02-22 15:55:59,528][11949] Decorrelating experience for 0 frames...
158
+ [2023-02-22 15:55:59,599][11950] Decorrelating experience for 0 frames...
159
+ [2023-02-22 15:55:59,600][11970] Decorrelating experience for 0 frames...
160
+ [2023-02-22 15:55:59,776][11951] Decorrelating experience for 32 frames...
161
+ [2023-02-22 15:55:59,801][11953] Decorrelating experience for 32 frames...
162
+ [2023-02-22 15:55:59,821][11975] Decorrelating experience for 0 frames...
163
+ [2023-02-22 15:55:59,852][11970] Decorrelating experience for 32 frames...
164
+ [2023-02-22 15:55:59,871][11950] Decorrelating experience for 32 frames...
165
+ [2023-02-22 15:55:59,887][11949] Decorrelating experience for 32 frames...
166
+ [2023-02-22 15:56:00,034][11973] Decorrelating experience for 32 frames...
167
+ [2023-02-22 15:56:00,121][11951] Decorrelating experience for 64 frames...
168
+ [2023-02-22 15:56:00,134][11975] Decorrelating experience for 32 frames...
169
+ [2023-02-22 15:56:00,157][11970] Decorrelating experience for 64 frames...
170
+ [2023-02-22 15:56:00,175][11950] Decorrelating experience for 64 frames...
171
+ [2023-02-22 15:56:00,349][11973] Decorrelating experience for 64 frames...
172
+ [2023-02-22 15:56:00,423][11953] Decorrelating experience for 64 frames...
173
+ [2023-02-22 15:56:00,427][11949] Decorrelating experience for 64 frames...
174
+ [2023-02-22 15:56:00,428][11951] Decorrelating experience for 96 frames...
175
+ [2023-02-22 15:56:00,460][11970] Decorrelating experience for 96 frames...
176
+ [2023-02-22 15:56:00,695][11950] Decorrelating experience for 96 frames...
177
+ [2023-02-22 15:56:00,714][11975] Decorrelating experience for 64 frames...
178
+ [2023-02-22 15:56:00,726][11949] Decorrelating experience for 96 frames...
179
+ [2023-02-22 15:56:00,736][11953] Decorrelating experience for 96 frames...
180
+ [2023-02-22 15:56:00,775][11973] Decorrelating experience for 96 frames...
181
+ [2023-02-22 15:56:00,999][11975] Decorrelating experience for 96 frames...
182
+ [2023-02-22 15:56:02,406][11727] 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)
183
+ [2023-02-22 15:56:05,026][11934] Signal inference workers to stop experience collection...
184
+ [2023-02-22 15:56:05,032][11948] InferenceWorker_p0-w0: stopping experience collection
185
+ [2023-02-22 15:56:06,229][11727] Heartbeat connected on Batcher_0
186
+ [2023-02-22 15:56:06,237][11727] Heartbeat connected on InferenceWorker_p0-w0
187
+ [2023-02-22 15:56:06,244][11727] Heartbeat connected on RolloutWorker_w0
188
+ [2023-02-22 15:56:06,247][11727] Heartbeat connected on RolloutWorker_w1
189
+ [2023-02-22 15:56:06,251][11727] Heartbeat connected on RolloutWorker_w2
190
+ [2023-02-22 15:56:06,254][11727] Heartbeat connected on RolloutWorker_w3
191
+ [2023-02-22 15:56:06,258][11727] Heartbeat connected on RolloutWorker_w4
192
+ [2023-02-22 15:56:06,261][11727] Heartbeat connected on RolloutWorker_w5
193
+ [2023-02-22 15:56:06,268][11727] Heartbeat connected on RolloutWorker_w7
194
+ [2023-02-22 15:56:07,406][11727] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 310.2. Samples: 3102. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
195
+ [2023-02-22 15:56:07,408][11727] Avg episode reward: [(0, '2.718')]
196
+ [2023-02-22 15:56:07,941][11934] Signal inference workers to resume experience collection...
197
+ [2023-02-22 15:56:07,941][11948] InferenceWorker_p0-w0: resuming experience collection
198
+ [2023-02-22 15:56:08,828][11727] Heartbeat connected on LearnerWorker_p0
199
+ [2023-02-22 15:56:10,521][11948] Updated weights for policy 0, policy_version 10 (0.0011)
200
+ [2023-02-22 15:56:12,406][11727] Fps is (10 sec: 6963.0, 60 sec: 4642.1, 300 sec: 4642.1). Total num frames: 69632. Throughput: 0: 901.7. Samples: 13526. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
201
+ [2023-02-22 15:56:12,408][11727] Avg episode reward: [(0, '4.505')]
202
+ [2023-02-22 15:56:12,915][11948] Updated weights for policy 0, policy_version 20 (0.0011)
203
+ [2023-02-22 15:56:15,191][11948] Updated weights for policy 0, policy_version 30 (0.0011)
204
+ [2023-02-22 15:56:17,406][11727] Fps is (10 sec: 15564.7, 60 sec: 7782.4, 300 sec: 7782.4). Total num frames: 155648. Throughput: 0: 1980.7. Samples: 39614. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
205
+ [2023-02-22 15:56:17,408][11727] Avg episode reward: [(0, '4.543')]
206
+ [2023-02-22 15:56:17,424][11934] Saving new best policy, reward=4.543!
207
+ [2023-02-22 15:56:17,653][11948] Updated weights for policy 0, policy_version 40 (0.0011)
208
+ [2023-02-22 15:56:20,028][11948] Updated weights for policy 0, policy_version 50 (0.0011)
209
+ [2023-02-22 15:56:22,406][11727] Fps is (10 sec: 17203.5, 60 sec: 9666.5, 300 sec: 9666.5). Total num frames: 241664. Throughput: 0: 2092.2. Samples: 52304. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
210
+ [2023-02-22 15:56:22,409][11727] Avg episode reward: [(0, '4.353')]
211
+ [2023-02-22 15:56:22,468][11948] Updated weights for policy 0, policy_version 60 (0.0011)
212
+ [2023-02-22 15:56:24,692][11948] Updated weights for policy 0, policy_version 70 (0.0011)
213
+ [2023-02-22 15:56:26,917][11948] Updated weights for policy 0, policy_version 80 (0.0011)
214
+ [2023-02-22 15:56:27,406][11727] Fps is (10 sec: 17612.8, 60 sec: 11059.2, 300 sec: 11059.2). Total num frames: 331776. Throughput: 0: 2634.7. Samples: 79042. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
215
+ [2023-02-22 15:56:27,408][11727] Avg episode reward: [(0, '4.552')]
216
+ [2023-02-22 15:56:27,412][11934] Saving new best policy, reward=4.552!
217
+ [2023-02-22 15:56:29,208][11948] Updated weights for policy 0, policy_version 90 (0.0011)
218
+ [2023-02-22 15:56:31,459][11948] Updated weights for policy 0, policy_version 100 (0.0010)
219
+ [2023-02-22 15:56:32,406][11727] Fps is (10 sec: 18432.0, 60 sec: 12171.0, 300 sec: 12171.0). Total num frames: 425984. Throughput: 0: 3030.2. Samples: 106058. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
220
+ [2023-02-22 15:56:32,408][11727] Avg episode reward: [(0, '4.755')]
221
+ [2023-02-22 15:56:32,417][11934] Saving new best policy, reward=4.755!
222
+ [2023-02-22 15:56:33,894][11948] Updated weights for policy 0, policy_version 110 (0.0011)
223
+ [2023-02-22 15:56:36,358][11948] Updated weights for policy 0, policy_version 120 (0.0012)
224
+ [2023-02-22 15:56:37,406][11727] Fps is (10 sec: 17612.8, 60 sec: 12697.6, 300 sec: 12697.6). Total num frames: 507904. Throughput: 0: 2967.0. Samples: 118682. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
225
+ [2023-02-22 15:56:37,408][11727] Avg episode reward: [(0, '4.517')]
226
+ [2023-02-22 15:56:38,646][11948] Updated weights for policy 0, policy_version 130 (0.0011)
227
+ [2023-02-22 15:56:40,961][11948] Updated weights for policy 0, policy_version 140 (0.0011)
228
+ [2023-02-22 15:56:42,406][11727] Fps is (10 sec: 17203.3, 60 sec: 13289.2, 300 sec: 13289.2). Total num frames: 598016. Throughput: 0: 3222.5. Samples: 145014. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
229
+ [2023-02-22 15:56:42,408][11727] Avg episode reward: [(0, '4.649')]
230
+ [2023-02-22 15:56:43,202][11948] Updated weights for policy 0, policy_version 150 (0.0011)
231
+ [2023-02-22 15:56:45,462][11948] Updated weights for policy 0, policy_version 160 (0.0011)
232
+ [2023-02-22 15:56:47,406][11727] Fps is (10 sec: 18022.4, 60 sec: 13762.6, 300 sec: 13762.6). Total num frames: 688128. Throughput: 0: 3827.0. Samples: 172214. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
233
+ [2023-02-22 15:56:47,409][11727] Avg episode reward: [(0, '4.915')]
234
+ [2023-02-22 15:56:47,413][11934] Saving new best policy, reward=4.915!
235
+ [2023-02-22 15:56:47,753][11948] Updated weights for policy 0, policy_version 170 (0.0011)
236
+ [2023-02-22 15:56:50,203][11948] Updated weights for policy 0, policy_version 180 (0.0011)
237
+ [2023-02-22 15:56:52,406][11727] Fps is (10 sec: 17612.7, 60 sec: 14075.3, 300 sec: 14075.3). Total num frames: 774144. Throughput: 0: 4038.2. Samples: 184820. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
238
+ [2023-02-22 15:56:52,409][11727] Avg episode reward: [(0, '5.185')]
239
+ [2023-02-22 15:56:52,417][11934] Saving new best policy, reward=5.185!
240
+ [2023-02-22 15:56:52,622][11948] Updated weights for policy 0, policy_version 190 (0.0011)
241
+ [2023-02-22 15:56:54,883][11948] Updated weights for policy 0, policy_version 200 (0.0011)
242
+ [2023-02-22 15:56:57,222][11948] Updated weights for policy 0, policy_version 210 (0.0011)
243
+ [2023-02-22 15:56:57,406][11727] Fps is (10 sec: 17203.3, 60 sec: 14336.0, 300 sec: 14336.0). Total num frames: 860160. Throughput: 0: 4388.8. Samples: 211022. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
244
+ [2023-02-22 15:56:57,409][11727] Avg episode reward: [(0, '5.563')]
245
+ [2023-02-22 15:56:57,425][11934] Saving new best policy, reward=5.563!
246
+ [2023-02-22 15:56:59,425][11948] Updated weights for policy 0, policy_version 220 (0.0010)
247
+ [2023-02-22 15:57:01,745][11948] Updated weights for policy 0, policy_version 230 (0.0011)
248
+ [2023-02-22 15:57:02,406][11727] Fps is (10 sec: 18022.5, 60 sec: 15906.1, 300 sec: 14682.6). Total num frames: 954368. Throughput: 0: 4411.3. Samples: 238122. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
249
+ [2023-02-22 15:57:02,409][11727] Avg episode reward: [(0, '6.131')]
250
+ [2023-02-22 15:57:02,415][11934] Saving new best policy, reward=6.131!
251
+ [2023-02-22 15:57:04,074][11948] Updated weights for policy 0, policy_version 240 (0.0011)
252
+ [2023-02-22 15:57:06,481][11948] Updated weights for policy 0, policy_version 250 (0.0011)
253
+ [2023-02-22 15:57:07,406][11727] Fps is (10 sec: 17612.8, 60 sec: 17271.5, 300 sec: 14804.1). Total num frames: 1036288. Throughput: 0: 4415.2. Samples: 250988. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
254
+ [2023-02-22 15:57:07,409][11727] Avg episode reward: [(0, '6.780')]
255
+ [2023-02-22 15:57:07,427][11934] Saving new best policy, reward=6.780!
256
+ [2023-02-22 15:57:08,893][11948] Updated weights for policy 0, policy_version 260 (0.0011)
257
+ [2023-02-22 15:57:11,132][11948] Updated weights for policy 0, policy_version 270 (0.0010)
258
+ [2023-02-22 15:57:12,406][11727] Fps is (10 sec: 17203.3, 60 sec: 17612.9, 300 sec: 15018.7). Total num frames: 1126400. Throughput: 0: 4406.4. Samples: 277330. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
259
+ [2023-02-22 15:57:12,408][11727] Avg episode reward: [(0, '7.336')]
260
+ [2023-02-22 15:57:12,417][11934] Saving new best policy, reward=7.336!
261
+ [2023-02-22 15:57:13,391][11948] Updated weights for policy 0, policy_version 280 (0.0012)
262
+ [2023-02-22 15:57:15,635][11948] Updated weights for policy 0, policy_version 290 (0.0010)
263
+ [2023-02-22 15:57:17,406][11727] Fps is (10 sec: 18022.3, 60 sec: 17681.1, 300 sec: 15206.4). Total num frames: 1216512. Throughput: 0: 4410.0. Samples: 304508. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
264
+ [2023-02-22 15:57:17,408][11727] Avg episode reward: [(0, '7.986')]
265
+ [2023-02-22 15:57:17,419][11934] Saving new best policy, reward=7.986!
266
+ [2023-02-22 15:57:17,940][11948] Updated weights for policy 0, policy_version 300 (0.0011)
267
+ [2023-02-22 15:57:20,251][11948] Updated weights for policy 0, policy_version 310 (0.0011)
268
+ [2023-02-22 15:57:22,406][11727] Fps is (10 sec: 17612.6, 60 sec: 17681.1, 300 sec: 15323.9). Total num frames: 1302528. Throughput: 0: 4421.6. Samples: 317652. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
269
+ [2023-02-22 15:57:22,408][11727] Avg episode reward: [(0, '9.339')]
270
+ [2023-02-22 15:57:22,419][11934] Saving new best policy, reward=9.339!
271
+ [2023-02-22 15:57:22,705][11948] Updated weights for policy 0, policy_version 320 (0.0012)
272
+ [2023-02-22 15:57:25,079][11948] Updated weights for policy 0, policy_version 330 (0.0011)
273
+ [2023-02-22 15:57:27,406][11727] Fps is (10 sec: 17203.4, 60 sec: 17612.8, 300 sec: 15428.3). Total num frames: 1388544. Throughput: 0: 4407.8. Samples: 343364. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
274
+ [2023-02-22 15:57:27,408][11727] Avg episode reward: [(0, '9.500')]
275
+ [2023-02-22 15:57:27,420][11934] Saving new best policy, reward=9.500!
276
+ [2023-02-22 15:57:27,422][11948] Updated weights for policy 0, policy_version 340 (0.0011)
277
+ [2023-02-22 15:57:29,748][11948] Updated weights for policy 0, policy_version 350 (0.0011)
278
+ [2023-02-22 15:57:32,048][11948] Updated weights for policy 0, policy_version 360 (0.0010)
279
+ [2023-02-22 15:57:32,406][11727] Fps is (10 sec: 17612.9, 60 sec: 17544.5, 300 sec: 15564.8). Total num frames: 1478656. Throughput: 0: 4389.4. Samples: 369738. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
280
+ [2023-02-22 15:57:32,408][11727] Avg episode reward: [(0, '10.590')]
281
+ [2023-02-22 15:57:32,416][11934] Saving new best policy, reward=10.590!
282
+ [2023-02-22 15:57:34,420][11948] Updated weights for policy 0, policy_version 370 (0.0011)
283
+ [2023-02-22 15:57:36,777][11948] Updated weights for policy 0, policy_version 380 (0.0011)
284
+ [2023-02-22 15:57:37,406][11727] Fps is (10 sec: 17612.8, 60 sec: 17612.8, 300 sec: 15646.7). Total num frames: 1564672. Throughput: 0: 4400.5. Samples: 382842. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
285
+ [2023-02-22 15:57:37,408][11727] Avg episode reward: [(0, '11.263')]
286
+ [2023-02-22 15:57:37,412][11934] Saving new best policy, reward=11.263!
287
+ [2023-02-22 15:57:39,255][11948] Updated weights for policy 0, policy_version 390 (0.0012)
288
+ [2023-02-22 15:57:41,601][11948] Updated weights for policy 0, policy_version 400 (0.0011)
289
+ [2023-02-22 15:57:42,406][11727] Fps is (10 sec: 17203.1, 60 sec: 17544.5, 300 sec: 15720.8). Total num frames: 1650688. Throughput: 0: 4381.9. Samples: 408208. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
290
+ [2023-02-22 15:57:42,408][11727] Avg episode reward: [(0, '11.024')]
291
+ [2023-02-22 15:57:42,417][11934] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000403_1650688.pth...
292
+ [2023-02-22 15:57:43,882][11948] Updated weights for policy 0, policy_version 410 (0.0018)
293
+ [2023-02-22 15:57:46,152][11948] Updated weights for policy 0, policy_version 420 (0.0011)
294
+ [2023-02-22 15:57:47,406][11727] Fps is (10 sec: 17612.9, 60 sec: 17544.6, 300 sec: 15825.5). Total num frames: 1740800. Throughput: 0: 4378.0. Samples: 435132. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
295
+ [2023-02-22 15:57:47,408][11727] Avg episode reward: [(0, '14.537')]
296
+ [2023-02-22 15:57:47,410][11934] Saving new best policy, reward=14.537!
297
+ [2023-02-22 15:57:48,472][11948] Updated weights for policy 0, policy_version 430 (0.0011)
298
+ [2023-02-22 15:57:50,720][11948] Updated weights for policy 0, policy_version 440 (0.0011)
299
+ [2023-02-22 15:57:52,406][11727] Fps is (10 sec: 17612.9, 60 sec: 17544.6, 300 sec: 15885.4). Total num frames: 1826816. Throughput: 0: 4394.8. Samples: 448752. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
300
+ [2023-02-22 15:57:52,409][11727] Avg episode reward: [(0, '15.374')]
301
+ [2023-02-22 15:57:52,415][11934] Saving new best policy, reward=15.374!
302
+ [2023-02-22 15:57:53,133][11948] Updated weights for policy 0, policy_version 450 (0.0011)
303
+ [2023-02-22 15:57:55,519][11948] Updated weights for policy 0, policy_version 460 (0.0011)
304
+ [2023-02-22 15:57:57,406][11727] Fps is (10 sec: 17612.7, 60 sec: 17612.8, 300 sec: 15974.4). Total num frames: 1916928. Throughput: 0: 4381.9. Samples: 474514. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
305
+ [2023-02-22 15:57:57,409][11727] Avg episode reward: [(0, '17.365')]
306
+ [2023-02-22 15:57:57,411][11934] Saving new best policy, reward=17.365!
307
+ [2023-02-22 15:57:57,797][11948] Updated weights for policy 0, policy_version 470 (0.0011)
308
+ [2023-02-22 15:58:00,064][11948] Updated weights for policy 0, policy_version 480 (0.0011)
309
+ [2023-02-22 15:58:02,345][11948] Updated weights for policy 0, policy_version 490 (0.0010)
310
+ [2023-02-22 15:58:02,406][11727] Fps is (10 sec: 18022.6, 60 sec: 17544.6, 300 sec: 16056.3). Total num frames: 2007040. Throughput: 0: 4381.5. Samples: 501676. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
311
+ [2023-02-22 15:58:02,409][11727] Avg episode reward: [(0, '15.039')]
312
+ [2023-02-22 15:58:04,589][11948] Updated weights for policy 0, policy_version 500 (0.0011)
313
+ [2023-02-22 15:58:06,878][11948] Updated weights for policy 0, policy_version 510 (0.0011)
314
+ [2023-02-22 15:58:07,406][11727] Fps is (10 sec: 17612.8, 60 sec: 17612.8, 300 sec: 16100.4). Total num frames: 2093056. Throughput: 0: 4393.0. Samples: 515336. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
315
+ [2023-02-22 15:58:07,409][11727] Avg episode reward: [(0, '16.264')]
316
+ [2023-02-22 15:58:09,322][11948] Updated weights for policy 0, policy_version 520 (0.0011)
317
+ [2023-02-22 15:58:11,720][11948] Updated weights for policy 0, policy_version 530 (0.0011)
318
+ [2023-02-22 15:58:12,406][11727] Fps is (10 sec: 17203.1, 60 sec: 17544.5, 300 sec: 16141.3). Total num frames: 2179072. Throughput: 0: 4393.1. Samples: 541052. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
319
+ [2023-02-22 15:58:12,409][11727] Avg episode reward: [(0, '16.818')]
320
+ [2023-02-22 15:58:14,040][11948] Updated weights for policy 0, policy_version 540 (0.0011)
321
+ [2023-02-22 15:58:16,254][11948] Updated weights for policy 0, policy_version 550 (0.0011)
322
+ [2023-02-22 15:58:17,406][11727] Fps is (10 sec: 17612.7, 60 sec: 17544.5, 300 sec: 16208.5). Total num frames: 2269184. Throughput: 0: 4403.9. Samples: 567914. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
323
+ [2023-02-22 15:58:17,409][11727] Avg episode reward: [(0, '17.111')]
324
+ [2023-02-22 15:58:18,537][11948] Updated weights for policy 0, policy_version 560 (0.0011)
325
+ [2023-02-22 15:58:20,818][11948] Updated weights for policy 0, policy_version 570 (0.0011)
326
+ [2023-02-22 15:58:22,406][11727] Fps is (10 sec: 18432.0, 60 sec: 17681.1, 300 sec: 16299.3). Total num frames: 2363392. Throughput: 0: 4414.4. Samples: 581488. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
327
+ [2023-02-22 15:58:22,408][11727] Avg episode reward: [(0, '18.845')]
328
+ [2023-02-22 15:58:22,417][11934] Saving new best policy, reward=18.845!
329
+ [2023-02-22 15:58:23,129][11948] Updated weights for policy 0, policy_version 580 (0.0011)
330
+ [2023-02-22 15:58:25,602][11948] Updated weights for policy 0, policy_version 590 (0.0011)
331
+ [2023-02-22 15:58:27,406][11727] Fps is (10 sec: 17613.0, 60 sec: 17612.8, 300 sec: 16302.1). Total num frames: 2445312. Throughput: 0: 4420.1. Samples: 607112. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
332
+ [2023-02-22 15:58:27,409][11727] Avg episode reward: [(0, '17.650')]
333
+ [2023-02-22 15:58:28,042][11948] Updated weights for policy 0, policy_version 600 (0.0012)
334
+ [2023-02-22 15:58:30,381][11948] Updated weights for policy 0, policy_version 610 (0.0011)
335
+ [2023-02-22 15:58:32,406][11727] Fps is (10 sec: 16793.4, 60 sec: 17544.5, 300 sec: 16331.1). Total num frames: 2531328. Throughput: 0: 4401.9. Samples: 633220. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
336
+ [2023-02-22 15:58:32,409][11727] Avg episode reward: [(0, '18.158')]
337
+ [2023-02-22 15:58:32,688][11948] Updated weights for policy 0, policy_version 620 (0.0011)
338
+ [2023-02-22 15:58:35,144][11948] Updated weights for policy 0, policy_version 630 (0.0011)
339
+ [2023-02-22 15:58:37,406][11727] Fps is (10 sec: 17203.0, 60 sec: 17544.5, 300 sec: 16358.4). Total num frames: 2617344. Throughput: 0: 4381.1. Samples: 645902. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
340
+ [2023-02-22 15:58:37,409][11727] Avg episode reward: [(0, '17.190')]
341
+ [2023-02-22 15:58:37,540][11948] Updated weights for policy 0, policy_version 640 (0.0011)
342
+ [2023-02-22 15:58:40,020][11948] Updated weights for policy 0, policy_version 650 (0.0012)
343
+ [2023-02-22 15:58:42,406][11727] Fps is (10 sec: 16793.8, 60 sec: 17476.3, 300 sec: 16359.2). Total num frames: 2699264. Throughput: 0: 4369.1. Samples: 671124. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
344
+ [2023-02-22 15:58:42,410][11727] Avg episode reward: [(0, '20.379')]
345
+ [2023-02-22 15:58:42,443][11934] Saving new best policy, reward=20.379!
346
+ [2023-02-22 15:58:42,447][11948] Updated weights for policy 0, policy_version 660 (0.0011)
347
+ [2023-02-22 15:58:44,798][11948] Updated weights for policy 0, policy_version 670 (0.0012)
348
+ [2023-02-22 15:58:47,071][11948] Updated weights for policy 0, policy_version 680 (0.0010)
349
+ [2023-02-22 15:58:47,406][11727] Fps is (10 sec: 17203.2, 60 sec: 17476.2, 300 sec: 16408.1). Total num frames: 2789376. Throughput: 0: 4347.0. Samples: 697292. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
350
+ [2023-02-22 15:58:47,409][11727] Avg episode reward: [(0, '20.186')]
351
+ [2023-02-22 15:58:49,407][11948] Updated weights for policy 0, policy_version 690 (0.0010)
352
+ [2023-02-22 15:58:51,689][11948] Updated weights for policy 0, policy_version 700 (0.0011)
353
+ [2023-02-22 15:58:52,406][11727] Fps is (10 sec: 18022.2, 60 sec: 17544.5, 300 sec: 16454.2). Total num frames: 2879488. Throughput: 0: 4340.9. Samples: 710678. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
354
+ [2023-02-22 15:58:52,408][11727] Avg episode reward: [(0, '22.052')]
355
+ [2023-02-22 15:58:52,417][11934] Saving new best policy, reward=22.052!
356
+ [2023-02-22 15:58:54,011][11948] Updated weights for policy 0, policy_version 710 (0.0011)
357
+ [2023-02-22 15:58:56,392][11948] Updated weights for policy 0, policy_version 720 (0.0011)
358
+ [2023-02-22 15:58:57,406][11727] Fps is (10 sec: 17613.0, 60 sec: 17476.3, 300 sec: 16475.0). Total num frames: 2965504. Throughput: 0: 4353.2. Samples: 736946. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
359
+ [2023-02-22 15:58:57,409][11727] Avg episode reward: [(0, '21.568')]
360
+ [2023-02-22 15:58:58,776][11948] Updated weights for policy 0, policy_version 730 (0.0011)
361
+ [2023-02-22 15:59:01,128][11948] Updated weights for policy 0, policy_version 740 (0.0010)
362
+ [2023-02-22 15:59:02,406][11727] Fps is (10 sec: 17203.3, 60 sec: 17408.0, 300 sec: 16494.7). Total num frames: 3051520. Throughput: 0: 4339.9. Samples: 763210. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
363
+ [2023-02-22 15:59:02,409][11727] Avg episode reward: [(0, '20.665')]
364
+ [2023-02-22 15:59:03,339][11948] Updated weights for policy 0, policy_version 750 (0.0011)
365
+ [2023-02-22 15:59:05,685][11948] Updated weights for policy 0, policy_version 760 (0.0011)
366
+ [2023-02-22 15:59:07,406][11727] Fps is (10 sec: 17612.6, 60 sec: 17476.3, 300 sec: 16534.9). Total num frames: 3141632. Throughput: 0: 4337.4. Samples: 776670. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
367
+ [2023-02-22 15:59:07,409][11727] Avg episode reward: [(0, '21.001')]
368
+ [2023-02-22 15:59:07,943][11948] Updated weights for policy 0, policy_version 770 (0.0010)
369
+ [2023-02-22 15:59:10,233][11948] Updated weights for policy 0, policy_version 780 (0.0011)
370
+ [2023-02-22 15:59:12,406][11727] Fps is (10 sec: 17612.8, 60 sec: 17476.3, 300 sec: 16552.0). Total num frames: 3227648. Throughput: 0: 4356.0. Samples: 803132. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
371
+ [2023-02-22 15:59:12,408][11727] Avg episode reward: [(0, '21.359')]
372
+ [2023-02-22 15:59:12,713][11948] Updated weights for policy 0, policy_version 790 (0.0012)
373
+ [2023-02-22 15:59:15,093][11948] Updated weights for policy 0, policy_version 800 (0.0011)
374
+ [2023-02-22 15:59:17,407][11948] Updated weights for policy 0, policy_version 810 (0.0011)
375
+ [2023-02-22 15:59:17,406][11727] Fps is (10 sec: 17613.2, 60 sec: 17476.3, 300 sec: 16588.8). Total num frames: 3317760. Throughput: 0: 4351.6. Samples: 829042. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
376
+ [2023-02-22 15:59:17,409][11727] Avg episode reward: [(0, '19.476')]
377
+ [2023-02-22 15:59:19,655][11948] Updated weights for policy 0, policy_version 820 (0.0011)
378
+ [2023-02-22 15:59:21,941][11948] Updated weights for policy 0, policy_version 830 (0.0010)
379
+ [2023-02-22 15:59:22,409][11727] Fps is (10 sec: 18016.5, 60 sec: 17407.0, 300 sec: 16623.5). Total num frames: 3407872. Throughput: 0: 4370.1. Samples: 842572. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
380
+ [2023-02-22 15:59:22,411][11727] Avg episode reward: [(0, '19.382')]
381
+ [2023-02-22 15:59:24,222][11948] Updated weights for policy 0, policy_version 840 (0.0011)
382
+ [2023-02-22 15:59:26,601][11948] Updated weights for policy 0, policy_version 850 (0.0011)
383
+ [2023-02-22 15:59:27,406][11727] Fps is (10 sec: 17612.3, 60 sec: 17476.2, 300 sec: 16637.6). Total num frames: 3493888. Throughput: 0: 4403.1. Samples: 869264. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
384
+ [2023-02-22 15:59:27,409][11727] Avg episode reward: [(0, '19.677')]
385
+ [2023-02-22 15:59:29,011][11948] Updated weights for policy 0, policy_version 860 (0.0012)
386
+ [2023-02-22 15:59:31,383][11948] Updated weights for policy 0, policy_version 870 (0.0011)
387
+ [2023-02-22 15:59:32,406][11727] Fps is (10 sec: 17208.8, 60 sec: 17476.3, 300 sec: 16650.7). Total num frames: 3579904. Throughput: 0: 4391.0. Samples: 894886. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
388
+ [2023-02-22 15:59:32,409][11727] Avg episode reward: [(0, '20.009')]
389
+ [2023-02-22 15:59:33,716][11948] Updated weights for policy 0, policy_version 880 (0.0011)
390
+ [2023-02-22 15:59:35,972][11948] Updated weights for policy 0, policy_version 890 (0.0011)
391
+ [2023-02-22 15:59:37,406][11727] Fps is (10 sec: 17612.9, 60 sec: 17544.5, 300 sec: 16681.9). Total num frames: 3670016. Throughput: 0: 4391.6. Samples: 908300. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
392
+ [2023-02-22 15:59:37,408][11727] Avg episode reward: [(0, '19.299')]
393
+ [2023-02-22 15:59:38,317][11948] Updated weights for policy 0, policy_version 900 (0.0011)
394
+ [2023-02-22 15:59:40,527][11948] Updated weights for policy 0, policy_version 910 (0.0011)
395
+ [2023-02-22 15:59:42,406][11727] Fps is (10 sec: 18022.4, 60 sec: 17681.1, 300 sec: 16711.7). Total num frames: 3760128. Throughput: 0: 4407.5. Samples: 935284. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
396
+ [2023-02-22 15:59:42,409][11727] Avg episode reward: [(0, '21.332')]
397
+ [2023-02-22 15:59:42,417][11934] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000918_3760128.pth...
398
+ [2023-02-22 15:59:42,893][11948] Updated weights for policy 0, policy_version 920 (0.0011)
399
+ [2023-02-22 15:59:45,384][11948] Updated weights for policy 0, policy_version 930 (0.0012)
400
+ [2023-02-22 15:59:47,406][11727] Fps is (10 sec: 17203.2, 60 sec: 17544.5, 300 sec: 16704.6). Total num frames: 3842048. Throughput: 0: 4387.0. Samples: 960626. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
401
+ [2023-02-22 15:59:47,409][11727] Avg episode reward: [(0, '19.742')]
402
+ [2023-02-22 15:59:47,743][11948] Updated weights for policy 0, policy_version 940 (0.0011)
403
+ [2023-02-22 15:59:50,051][11948] Updated weights for policy 0, policy_version 950 (0.0011)
404
+ [2023-02-22 15:59:52,331][11948] Updated weights for policy 0, policy_version 960 (0.0011)
405
+ [2023-02-22 15:59:52,406][11727] Fps is (10 sec: 17203.2, 60 sec: 17544.5, 300 sec: 16732.6). Total num frames: 3932160. Throughput: 0: 4384.1. Samples: 973954. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
406
+ [2023-02-22 15:59:52,408][11727] Avg episode reward: [(0, '23.771')]
407
+ [2023-02-22 15:59:52,417][11934] Saving new best policy, reward=23.771!
408
+ [2023-02-22 15:59:54,622][11948] Updated weights for policy 0, policy_version 970 (0.0011)
409
+ [2023-02-22 15:59:56,458][11934] Stopping Batcher_0...
410
+ [2023-02-22 15:59:56,459][11934] Loop batcher_evt_loop terminating...
411
+ [2023-02-22 15:59:56,459][11934] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
412
+ [2023-02-22 15:59:56,459][11727] Component Batcher_0 stopped!
413
+ [2023-02-22 15:59:56,462][11727] Component RolloutWorker_w6 process died already! Don't wait for it.
414
+ [2023-02-22 15:59:56,473][11948] Weights refcount: 2 0
415
+ [2023-02-22 15:59:56,473][11951] Stopping RolloutWorker_w2...
416
+ [2023-02-22 15:59:56,473][11950] Stopping RolloutWorker_w1...
417
+ [2023-02-22 15:59:56,474][11950] Loop rollout_proc1_evt_loop terminating...
418
+ [2023-02-22 15:59:56,474][11951] Loop rollout_proc2_evt_loop terminating...
419
+ [2023-02-22 15:59:56,475][11948] Stopping InferenceWorker_p0-w0...
420
+ [2023-02-22 15:59:56,475][11948] Loop inference_proc0-0_evt_loop terminating...
421
+ [2023-02-22 15:59:56,473][11727] Component RolloutWorker_w1 stopped!
422
+ [2023-02-22 15:59:56,476][11970] Stopping RolloutWorker_w5...
423
+ [2023-02-22 15:59:56,476][11953] Stopping RolloutWorker_w3...
424
+ [2023-02-22 15:59:56,476][11970] Loop rollout_proc5_evt_loop terminating...
425
+ [2023-02-22 15:59:56,476][11953] Loop rollout_proc3_evt_loop terminating...
426
+ [2023-02-22 15:59:56,476][11975] Stopping RolloutWorker_w4...
427
+ [2023-02-22 15:59:56,477][11975] Loop rollout_proc4_evt_loop terminating...
428
+ [2023-02-22 15:59:56,478][11949] Stopping RolloutWorker_w0...
429
+ [2023-02-22 15:59:56,479][11949] Loop rollout_proc0_evt_loop terminating...
430
+ [2023-02-22 15:59:56,477][11727] Component RolloutWorker_w2 stopped!
431
+ [2023-02-22 15:59:56,480][11727] Component InferenceWorker_p0-w0 stopped!
432
+ [2023-02-22 15:59:56,481][11727] Component RolloutWorker_w5 stopped!
433
+ [2023-02-22 15:59:56,482][11727] Component RolloutWorker_w3 stopped!
434
+ [2023-02-22 15:59:56,484][11727] Component RolloutWorker_w4 stopped!
435
+ [2023-02-22 15:59:56,484][11973] Stopping RolloutWorker_w7...
436
+ [2023-02-22 15:59:56,485][11727] Component RolloutWorker_w0 stopped!
437
+ [2023-02-22 15:59:56,486][11973] Loop rollout_proc7_evt_loop terminating...
438
+ [2023-02-22 15:59:56,487][11727] Component RolloutWorker_w7 stopped!
439
+ [2023-02-22 15:59:56,533][11934] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000403_1650688.pth
440
+ [2023-02-22 15:59:56,542][11934] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
441
+ [2023-02-22 15:59:56,658][11934] Stopping LearnerWorker_p0...
442
+ [2023-02-22 15:59:56,659][11934] Loop learner_proc0_evt_loop terminating...
443
+ [2023-02-22 15:59:56,658][11727] Component LearnerWorker_p0 stopped!
444
+ [2023-02-22 15:59:56,660][11727] Waiting for process learner_proc0 to stop...
445
+ [2023-02-22 15:59:58,235][11727] Waiting for process inference_proc0-0 to join...
446
+ [2023-02-22 15:59:58,237][11727] Waiting for process rollout_proc0 to join...
447
+ [2023-02-22 15:59:58,239][11727] Waiting for process rollout_proc1 to join...
448
+ [2023-02-22 15:59:58,241][11727] Waiting for process rollout_proc2 to join...
449
+ [2023-02-22 15:59:58,243][11727] Waiting for process rollout_proc3 to join...
450
+ [2023-02-22 15:59:58,245][11727] Waiting for process rollout_proc4 to join...
451
+ [2023-02-22 15:59:58,246][11727] Waiting for process rollout_proc5 to join...
452
+ [2023-02-22 15:59:58,248][11727] Waiting for process rollout_proc6 to join...
453
+ [2023-02-22 15:59:58,249][11727] Waiting for process rollout_proc7 to join...
454
+ [2023-02-22 15:59:58,252][11727] Batcher 0 profile tree view:
455
+ batching: 15.6119, releasing_batches: 0.0487
456
+ [2023-02-22 15:59:58,253][11727] InferenceWorker_p0-w0 profile tree view:
457
+ wait_policy: 0.0001
458
+ wait_policy_total: 4.2324
459
+ update_model: 3.4167
460
+ weight_update: 0.0011
461
+ one_step: 0.0029
462
+ handle_policy_step: 214.4727
463
+ deserialize: 8.7479, stack: 1.4146, obs_to_device_normalize: 50.9557, forward: 97.6511, send_messages: 15.7666
464
+ prepare_outputs: 30.0742
465
+ to_cpu: 18.3660
466
+ [2023-02-22 15:59:58,254][11727] Learner 0 profile tree view:
467
+ misc: 0.0057, prepare_batch: 10.1041
468
+ train: 19.8425
469
+ epoch_init: 0.0057, minibatch_init: 0.0062, losses_postprocess: 0.3212, kl_divergence: 0.4617, after_optimizer: 1.0259
470
+ calculate_losses: 7.7728
471
+ losses_init: 0.0032, forward_head: 1.1080, bptt_initial: 3.2464, tail: 0.6356, advantages_returns: 0.1694, losses: 1.0630
472
+ bptt: 1.3701
473
+ bptt_forward_core: 1.3165
474
+ update: 9.9027
475
+ clip: 1.1259
476
+ [2023-02-22 15:59:58,257][11727] RolloutWorker_w0 profile tree view:
477
+ wait_for_trajectories: 0.1715, enqueue_policy_requests: 8.7683, env_step: 144.6926, overhead: 11.5534, complete_rollouts: 0.2874
478
+ save_policy_outputs: 9.9906
479
+ split_output_tensors: 4.7988
480
+ [2023-02-22 15:59:58,258][11727] RolloutWorker_w7 profile tree view:
481
+ wait_for_trajectories: 0.1719, enqueue_policy_requests: 8.7025, env_step: 145.0611, overhead: 11.7300, complete_rollouts: 0.2973
482
+ save_policy_outputs: 9.8243
483
+ split_output_tensors: 4.7671
484
+ [2023-02-22 15:59:58,260][11727] Loop Runner_EvtLoop terminating...
485
+ [2023-02-22 15:59:58,263][11727] Runner profile tree view:
486
+ main_loop: 251.9949
487
+ [2023-02-22 15:59:58,264][11727] Collected {0: 4005888}, FPS: 15896.7
488
+ [2023-02-22 16:11:15,029][11727] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json
489
+ [2023-02-22 16:11:15,031][11727] Overriding arg 'num_workers' with value 1 passed from command line
490
+ [2023-02-22 16:11:15,033][11727] Adding new argument 'no_render'=True that is not in the saved config file!
491
+ [2023-02-22 16:11:15,034][11727] Adding new argument 'save_video'=True that is not in the saved config file!
492
+ [2023-02-22 16:11:15,036][11727] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
493
+ [2023-02-22 16:11:15,037][11727] Adding new argument 'video_name'=None that is not in the saved config file!
494
+ [2023-02-22 16:11:15,038][11727] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file!
495
+ [2023-02-22 16:11:15,040][11727] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
496
+ [2023-02-22 16:11:15,041][11727] Adding new argument 'push_to_hub'=False that is not in the saved config file!
497
+ [2023-02-22 16:11:15,043][11727] Adding new argument 'hf_repository'=None that is not in the saved config file!
498
+ [2023-02-22 16:11:15,044][11727] Adding new argument 'policy_index'=0 that is not in the saved config file!
499
+ [2023-02-22 16:11:15,045][11727] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
500
+ [2023-02-22 16:11:15,047][11727] Adding new argument 'train_script'=None that is not in the saved config file!
501
+ [2023-02-22 16:11:15,048][11727] Adding new argument 'enjoy_script'=None that is not in the saved config file!
502
+ [2023-02-22 16:11:15,049][11727] Using frameskip 1 and render_action_repeat=4 for evaluation
503
+ [2023-02-22 16:11:15,067][11727] Doom resolution: 160x120, resize resolution: (128, 72)
504
+ [2023-02-22 16:11:15,070][11727] RunningMeanStd input shape: (3, 72, 128)
505
+ [2023-02-22 16:11:15,073][11727] RunningMeanStd input shape: (1,)
506
+ [2023-02-22 16:11:15,093][11727] ConvEncoder: input_channels=3
507
+ [2023-02-22 16:11:15,967][11727] Conv encoder output size: 512
508
+ [2023-02-22 16:11:15,970][11727] Policy head output size: 512
509
+ [2023-02-22 16:11:18,855][11727] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
510
+ [2023-02-22 16:11:20,688][11727] Num frames 100...
511
+ [2023-02-22 16:11:20,811][11727] Num frames 200...
512
+ [2023-02-22 16:11:20,926][11727] Num frames 300...
513
+ [2023-02-22 16:11:21,038][11727] Num frames 400...
514
+ [2023-02-22 16:11:21,156][11727] Num frames 500...
515
+ [2023-02-22 16:11:21,269][11727] Num frames 600...
516
+ [2023-02-22 16:11:21,382][11727] Num frames 700...
517
+ [2023-02-22 16:11:21,496][11727] Num frames 800...
518
+ [2023-02-22 16:11:21,608][11727] Num frames 900...
519
+ [2023-02-22 16:11:21,720][11727] Num frames 1000...
520
+ [2023-02-22 16:11:21,833][11727] Num frames 1100...
521
+ [2023-02-22 16:11:21,947][11727] Avg episode rewards: #0: 24.520, true rewards: #0: 11.520
522
+ [2023-02-22 16:11:21,948][11727] Avg episode reward: 24.520, avg true_objective: 11.520
523
+ [2023-02-22 16:11:22,005][11727] Num frames 1200...
524
+ [2023-02-22 16:11:22,118][11727] Num frames 1300...
525
+ [2023-02-22 16:11:22,232][11727] Num frames 1400...
526
+ [2023-02-22 16:11:22,346][11727] Num frames 1500...
527
+ [2023-02-22 16:11:22,457][11727] Num frames 1600...
528
+ [2023-02-22 16:11:22,568][11727] Num frames 1700...
529
+ [2023-02-22 16:11:22,681][11727] Num frames 1800...
530
+ [2023-02-22 16:11:22,794][11727] Num frames 1900...
531
+ [2023-02-22 16:11:22,904][11727] Num frames 2000...
532
+ [2023-02-22 16:11:23,020][11727] Num frames 2100...
533
+ [2023-02-22 16:11:23,138][11727] Num frames 2200...
534
+ [2023-02-22 16:11:23,252][11727] Num frames 2300...
535
+ [2023-02-22 16:11:23,365][11727] Num frames 2400...
536
+ [2023-02-22 16:11:23,484][11727] Num frames 2500...
537
+ [2023-02-22 16:11:23,601][11727] Num frames 2600...
538
+ [2023-02-22 16:11:23,718][11727] Num frames 2700...
539
+ [2023-02-22 16:11:23,803][11727] Avg episode rewards: #0: 34.130, true rewards: #0: 13.630
540
+ [2023-02-22 16:11:23,805][11727] Avg episode reward: 34.130, avg true_objective: 13.630
541
+ [2023-02-22 16:11:23,891][11727] Num frames 2800...
542
+ [2023-02-22 16:11:24,003][11727] Num frames 2900...
543
+ [2023-02-22 16:11:24,121][11727] Num frames 3000...
544
+ [2023-02-22 16:11:24,234][11727] Num frames 3100...
545
+ [2023-02-22 16:11:24,312][11727] Avg episode rewards: #0: 25.063, true rewards: #0: 10.397
546
+ [2023-02-22 16:11:24,313][11727] Avg episode reward: 25.063, avg true_objective: 10.397
547
+ [2023-02-22 16:11:24,406][11727] Num frames 3200...
548
+ [2023-02-22 16:11:24,518][11727] Num frames 3300...
549
+ [2023-02-22 16:11:24,680][11727] Avg episode rewards: #0: 19.732, true rewards: #0: 8.482
550
+ [2023-02-22 16:11:24,682][11727] Avg episode reward: 19.732, avg true_objective: 8.482
551
+ [2023-02-22 16:11:24,692][11727] Num frames 3400...
552
+ [2023-02-22 16:11:24,818][11727] Num frames 3500...
553
+ [2023-02-22 16:11:24,939][11727] Num frames 3600...
554
+ [2023-02-22 16:11:25,055][11727] Num frames 3700...
555
+ [2023-02-22 16:11:25,171][11727] Num frames 3800...
556
+ [2023-02-22 16:11:25,285][11727] Num frames 3900...
557
+ [2023-02-22 16:11:25,401][11727] Num frames 4000...
558
+ [2023-02-22 16:11:25,517][11727] Num frames 4100...
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+ [2023-02-22 16:11:25,635][11727] Num frames 4200...
560
+ [2023-02-22 16:11:25,749][11727] Num frames 4300...
561
+ [2023-02-22 16:11:25,864][11727] Num frames 4400...
562
+ [2023-02-22 16:11:25,984][11727] Num frames 4500...
563
+ [2023-02-22 16:11:26,099][11727] Num frames 4600...
564
+ [2023-02-22 16:11:26,238][11727] Num frames 4700...
565
+ [2023-02-22 16:11:26,362][11727] Num frames 4800...
566
+ [2023-02-22 16:11:26,482][11727] Num frames 4900...
567
+ [2023-02-22 16:11:26,602][11727] Num frames 5000...
568
+ [2023-02-22 16:11:26,720][11727] Num frames 5100...
569
+ [2023-02-22 16:11:26,822][11727] Avg episode rewards: #0: 24.078, true rewards: #0: 10.278
570
+ [2023-02-22 16:11:26,824][11727] Avg episode reward: 24.078, avg true_objective: 10.278
571
+ [2023-02-22 16:11:26,904][11727] Num frames 5200...
572
+ [2023-02-22 16:11:27,021][11727] Num frames 5300...
573
+ [2023-02-22 16:11:27,140][11727] Num frames 5400...
574
+ [2023-02-22 16:11:27,256][11727] Num frames 5500...
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+ [2023-02-22 16:11:27,369][11727] Num frames 5600...
576
+ [2023-02-22 16:11:27,479][11727] Num frames 5700...
577
+ [2023-02-22 16:11:27,587][11727] Avg episode rewards: #0: 22.078, true rewards: #0: 9.578
578
+ [2023-02-22 16:11:27,589][11727] Avg episode reward: 22.078, avg true_objective: 9.578
579
+ [2023-02-22 16:11:27,652][11727] Num frames 5800...
580
+ [2023-02-22 16:11:27,767][11727] Num frames 5900...
581
+ [2023-02-22 16:11:27,883][11727] Num frames 6000...
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+ [2023-02-22 16:11:27,996][11727] Num frames 6100...
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+ [2023-02-22 16:11:28,109][11727] Num frames 6200...
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+ [2023-02-22 16:11:28,225][11727] Num frames 6300...
585
+ [2023-02-22 16:11:28,338][11727] Num frames 6400...
586
+ [2023-02-22 16:11:28,451][11727] Avg episode rewards: #0: 21.073, true rewards: #0: 9.216
587
+ [2023-02-22 16:11:28,453][11727] Avg episode reward: 21.073, avg true_objective: 9.216
588
+ [2023-02-22 16:11:28,511][11727] Num frames 6500...
589
+ [2023-02-22 16:11:28,625][11727] Num frames 6600...
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+ [2023-02-22 16:11:28,743][11727] Num frames 6700...
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+ [2023-02-22 16:11:28,855][11727] Num frames 6800...
592
+ [2023-02-22 16:11:28,951][11727] Avg episode rewards: #0: 19.294, true rewards: #0: 8.544
593
+ [2023-02-22 16:11:28,953][11727] Avg episode reward: 19.294, avg true_objective: 8.544
594
+ [2023-02-22 16:11:29,031][11727] Num frames 6900...
595
+ [2023-02-22 16:11:29,146][11727] Num frames 7000...
596
+ [2023-02-22 16:11:29,286][11727] Num frames 7100...
597
+ [2023-02-22 16:11:29,396][11727] Num frames 7200...
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+ [2023-02-22 16:11:29,505][11727] Num frames 7300...
599
+ [2023-02-22 16:11:29,619][11727] Num frames 7400...
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+ [2023-02-22 16:11:29,732][11727] Num frames 7500...
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+ [2023-02-22 16:11:29,849][11727] Num frames 7600...
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+ [2023-02-22 16:11:29,963][11727] Num frames 7700...
603
+ [2023-02-22 16:11:30,076][11727] Num frames 7800...
604
+ [2023-02-22 16:11:30,143][11727] Avg episode rewards: #0: 19.677, true rewards: #0: 8.677
605
+ [2023-02-22 16:11:30,145][11727] Avg episode reward: 19.677, avg true_objective: 8.677
606
+ [2023-02-22 16:11:30,249][11727] Num frames 7900...
607
+ [2023-02-22 16:11:30,364][11727] Num frames 8000...
608
+ [2023-02-22 16:11:30,478][11727] Num frames 8100...
609
+ [2023-02-22 16:11:30,594][11727] Num frames 8200...
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+ [2023-02-22 16:11:30,707][11727] Num frames 8300...
611
+ [2023-02-22 16:11:30,822][11727] Num frames 8400...
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+ [2023-02-22 16:11:30,937][11727] Num frames 8500...
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+ [2023-02-22 16:11:31,049][11727] Num frames 8600...
614
+ [2023-02-22 16:11:31,162][11727] Num frames 8700...
615
+ [2023-02-22 16:11:31,277][11727] Num frames 8800...
616
+ [2023-02-22 16:11:31,371][11727] Avg episode rewards: #0: 19.533, true rewards: #0: 8.833
617
+ [2023-02-22 16:11:31,373][11727] Avg episode reward: 19.533, avg true_objective: 8.833
618
+ [2023-02-22 16:11:52,331][11727] Replay video saved to /content/train_dir/default_experiment/replay.mp4!
619
+ [2023-02-22 16:12:41,257][11727] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json
620
+ [2023-02-22 16:12:41,259][11727] Overriding arg 'num_workers' with value 1 passed from command line
621
+ [2023-02-22 16:12:41,260][11727] Adding new argument 'no_render'=True that is not in the saved config file!
622
+ [2023-02-22 16:12:41,261][11727] Adding new argument 'save_video'=True that is not in the saved config file!
623
+ [2023-02-22 16:12:41,264][11727] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
624
+ [2023-02-22 16:12:41,265][11727] Adding new argument 'video_name'=None that is not in the saved config file!
625
+ [2023-02-22 16:12:41,266][11727] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!
626
+ [2023-02-22 16:12:41,268][11727] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
627
+ [2023-02-22 16:12:41,270][11727] Adding new argument 'push_to_hub'=True that is not in the saved config file!
628
+ [2023-02-22 16:12:41,271][11727] Adding new argument 'hf_repository'='Unterwexi/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file!
629
+ [2023-02-22 16:12:41,272][11727] Adding new argument 'policy_index'=0 that is not in the saved config file!
630
+ [2023-02-22 16:12:41,274][11727] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
631
+ [2023-02-22 16:12:41,276][11727] Adding new argument 'train_script'=None that is not in the saved config file!
632
+ [2023-02-22 16:12:41,277][11727] Adding new argument 'enjoy_script'=None that is not in the saved config file!
633
+ [2023-02-22 16:12:41,279][11727] Using frameskip 1 and render_action_repeat=4 for evaluation
634
+ [2023-02-22 16:12:41,297][11727] RunningMeanStd input shape: (3, 72, 128)
635
+ [2023-02-22 16:12:41,300][11727] RunningMeanStd input shape: (1,)
636
+ [2023-02-22 16:12:41,315][11727] ConvEncoder: input_channels=3
637
+ [2023-02-22 16:12:41,358][11727] Conv encoder output size: 512
638
+ [2023-02-22 16:12:41,359][11727] Policy head output size: 512
639
+ [2023-02-22 16:12:41,382][11727] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
640
+ [2023-02-22 16:12:41,857][11727] Num frames 100...
641
+ [2023-02-22 16:12:41,979][11727] Num frames 200...
642
+ [2023-02-22 16:12:42,097][11727] Num frames 300...
643
+ [2023-02-22 16:12:42,233][11727] Avg episode rewards: #0: 7.670, true rewards: #0: 3.670
644
+ [2023-02-22 16:12:42,235][11727] Avg episode reward: 7.670, avg true_objective: 3.670
645
+ [2023-02-22 16:12:42,276][11727] Num frames 400...
646
+ [2023-02-22 16:12:42,396][11727] Num frames 500...
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+ [2023-02-22 16:12:42,528][11727] Num frames 600...
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+ [2023-02-22 16:12:42,653][11727] Num frames 700...
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+ [2023-02-22 16:12:42,780][11727] Num frames 800...
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+ [2023-02-22 16:12:42,907][11727] Num frames 900...
651
+ [2023-02-22 16:12:43,034][11727] Num frames 1000...
652
+ [2023-02-22 16:12:43,181][11727] Avg episode rewards: #0: 12.870, true rewards: #0: 5.370
653
+ [2023-02-22 16:12:43,183][11727] Avg episode reward: 12.870, avg true_objective: 5.370
654
+ [2023-02-22 16:12:43,215][11727] Num frames 1100...
655
+ [2023-02-22 16:12:43,331][11727] Num frames 1200...
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+ [2023-02-22 16:12:43,441][11727] Num frames 1300...
657
+ [2023-02-22 16:12:43,554][11727] Num frames 1400...
658
+ [2023-02-22 16:12:43,685][11727] Avg episode rewards: #0: 10.860, true rewards: #0: 4.860
659
+ [2023-02-22 16:12:43,687][11727] Avg episode reward: 10.860, avg true_objective: 4.860
660
+ [2023-02-22 16:12:43,745][11727] Num frames 1500...
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+ [2023-02-22 16:12:43,870][11727] Num frames 1600...
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+ [2023-02-22 16:12:43,992][11727] Num frames 1700...
663
+ [2023-02-22 16:12:44,116][11727] Num frames 1800...
664
+ [2023-02-22 16:12:44,184][11727] Avg episode rewards: #0: 9.775, true rewards: #0: 4.525
665
+ [2023-02-22 16:12:44,187][11727] Avg episode reward: 9.775, avg true_objective: 4.525
666
+ [2023-02-22 16:12:44,288][11727] Num frames 1900...
667
+ [2023-02-22 16:12:44,400][11727] Num frames 2000...
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+ [2023-02-22 16:12:44,510][11727] Num frames 2100...
669
+ [2023-02-22 16:12:44,622][11727] Num frames 2200...
670
+ [2023-02-22 16:12:44,709][11727] Avg episode rewards: #0: 9.052, true rewards: #0: 4.452
671
+ [2023-02-22 16:12:44,711][11727] Avg episode reward: 9.052, avg true_objective: 4.452
672
+ [2023-02-22 16:12:44,795][11727] Num frames 2300...
673
+ [2023-02-22 16:12:44,906][11727] Num frames 2400...
674
+ [2023-02-22 16:12:45,039][11727] Num frames 2500...
675
+ [2023-02-22 16:12:45,152][11727] Num frames 2600...
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+ [2023-02-22 16:12:45,268][11727] Num frames 2700...
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+ [2023-02-22 16:12:45,381][11727] Num frames 2800...
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+ [2023-02-22 16:12:45,492][11727] Num frames 2900...
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+ [2023-02-22 16:12:45,602][11727] Num frames 3000...
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+ [2023-02-22 16:12:45,715][11727] Num frames 3100...
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+ [2023-02-22 16:12:45,826][11727] Num frames 3200...
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+ [2023-02-22 16:12:45,936][11727] Num frames 3300...
683
+ [2023-02-22 16:12:46,050][11727] Num frames 3400...
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+ [2023-02-22 16:12:46,166][11727] Num frames 3500...
685
+ [2023-02-22 16:12:46,282][11727] Num frames 3600...
686
+ [2023-02-22 16:12:46,392][11727] Num frames 3700...
687
+ [2023-02-22 16:12:46,507][11727] Num frames 3800...
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+ [2023-02-22 16:12:46,618][11727] Num frames 3900...
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+ [2023-02-22 16:12:46,730][11727] Num frames 4000...
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+ [2023-02-22 16:12:46,845][11727] Num frames 4100...
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+ [2023-02-22 16:12:46,960][11727] Num frames 4200...
692
+ [2023-02-22 16:12:47,087][11727] Num frames 4300...
693
+ [2023-02-22 16:12:47,174][11727] Avg episode rewards: #0: 17.376, true rewards: #0: 7.210
694
+ [2023-02-22 16:12:47,176][11727] Avg episode reward: 17.376, avg true_objective: 7.210
695
+ [2023-02-22 16:12:47,260][11727] Num frames 4400...
696
+ [2023-02-22 16:12:47,373][11727] Num frames 4500...
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+ [2023-02-22 16:12:47,485][11727] Num frames 4600...
698
+ [2023-02-22 16:12:47,594][11727] Num frames 4700...
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+ [2023-02-22 16:12:47,706][11727] Num frames 4800...
700
+ [2023-02-22 16:12:47,817][11727] Num frames 4900...
701
+ [2023-02-22 16:12:47,929][11727] Num frames 5000...
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+ [2023-02-22 16:12:48,050][11727] Num frames 5100...
703
+ [2023-02-22 16:12:48,165][11727] Num frames 5200...
704
+ [2023-02-22 16:12:48,248][11727] Avg episode rewards: #0: 17.603, true rewards: #0: 7.460
705
+ [2023-02-22 16:12:48,250][11727] Avg episode reward: 17.603, avg true_objective: 7.460
706
+ [2023-02-22 16:12:48,337][11727] Num frames 5300...
707
+ [2023-02-22 16:12:48,446][11727] Num frames 5400...
708
+ [2023-02-22 16:12:48,557][11727] Num frames 5500...
709
+ [2023-02-22 16:12:48,668][11727] Num frames 5600...
710
+ [2023-02-22 16:12:48,779][11727] Num frames 5700...
711
+ [2023-02-22 16:12:48,891][11727] Num frames 5800...
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+ [2023-02-22 16:12:49,005][11727] Num frames 5900...
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+ [2023-02-22 16:12:49,116][11727] Num frames 6000...
714
+ [2023-02-22 16:12:49,225][11727] Num frames 6100...
715
+ [2023-02-22 16:12:49,303][11727] Avg episode rewards: #0: 17.522, true rewards: #0: 7.647
716
+ [2023-02-22 16:12:49,306][11727] Avg episode reward: 17.522, avg true_objective: 7.647
717
+ [2023-02-22 16:12:49,397][11727] Num frames 6200...
718
+ [2023-02-22 16:12:49,509][11727] Num frames 6300...
719
+ [2023-02-22 16:12:49,625][11727] Num frames 6400...
720
+ [2023-02-22 16:12:49,736][11727] Num frames 6500...
721
+ [2023-02-22 16:12:49,849][11727] Num frames 6600...
722
+ [2023-02-22 16:12:49,938][11727] Avg episode rewards: #0: 16.478, true rewards: #0: 7.367
723
+ [2023-02-22 16:12:49,940][11727] Avg episode reward: 16.478, avg true_objective: 7.367
724
+ [2023-02-22 16:12:50,021][11727] Num frames 6700...
725
+ [2023-02-22 16:12:50,134][11727] Num frames 6800...
726
+ [2023-02-22 16:12:50,248][11727] Num frames 6900...
727
+ [2023-02-22 16:12:50,360][11727] Num frames 7000...
728
+ [2023-02-22 16:12:50,470][11727] Num frames 7100...
729
+ [2023-02-22 16:12:50,579][11727] Num frames 7200...
730
+ [2023-02-22 16:12:50,690][11727] Num frames 7300...
731
+ [2023-02-22 16:12:50,803][11727] Num frames 7400...
732
+ [2023-02-22 16:12:50,929][11727] Avg episode rewards: #0: 16.461, true rewards: #0: 7.461
733
+ [2023-02-22 16:12:50,931][11727] Avg episode reward: 16.461, avg true_objective: 7.461
734
+ [2023-02-22 16:13:08,594][11727] Replay video saved to /content/train_dir/default_experiment/replay.mp4!