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Christian90
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rl_course_vizdoom_health_gathering_supreme

Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
Eval Results
Model card Files Metrics Community
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  9. replay.mp4 +3 -0
  10. sf_log.txt +1149 -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
6
+ - 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
15
+ type: doom_health_gathering_supreme
16
+ metrics:
17
+ - type: mean_reward
18
+ value: 11.68 +/- 4.74
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+ name: mean_reward
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+ verified: false
21
+ ---
22
+
23
+ A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
24
+
25
+ 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 Christian90/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
42
+ ```
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": "/home/ckahmann/RL/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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+ "async_rl": true,
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+ "num_batches_to_accumulate": 2,
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+ "max_policy_lag": 1000,
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+ "num_workers": 16,
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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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+ "actor_critic_share_weights": true,
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+ "adaptive_stddev": true,
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+ "wandb_project": "sample_factory",
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+ "wandb_group": null,
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+ "wandb_job_type": "SF",
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+ "wandb_tags": [],
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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_mutation_rate": 0.15,
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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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+ "num_bots": -1,
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+ "start_bot_difficulty": null,
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+ "timelimit": null,
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+ "res_w": 128,
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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",
134
+ "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
+ [2023-03-17 10:54:05,152][24380] Saving configuration to /home/ckahmann/RL/train_dir/default_experiment/config.json...
2
+ [2023-03-17 10:54:05,155][24380] Rollout worker 0 uses device cpu
3
+ [2023-03-17 10:54:05,156][24380] Rollout worker 1 uses device cpu
4
+ [2023-03-17 10:54:05,157][24380] Rollout worker 2 uses device cpu
5
+ [2023-03-17 10:54:05,159][24380] Rollout worker 3 uses device cpu
6
+ [2023-03-17 10:54:05,160][24380] Rollout worker 4 uses device cpu
7
+ [2023-03-17 10:54:05,161][24380] Rollout worker 5 uses device cpu
8
+ [2023-03-17 10:54:05,162][24380] Rollout worker 6 uses device cpu
9
+ [2023-03-17 10:54:05,163][24380] Rollout worker 7 uses device cpu
10
+ [2023-03-17 10:54:05,210][24380] Using GPUs [0] for process 0 (actually maps to GPUs [0])
11
+ [2023-03-17 10:54:05,211][24380] InferenceWorker_p0-w0: min num requests: 2
12
+ [2023-03-17 10:54:05,232][24380] Starting all processes...
13
+ [2023-03-17 10:54:05,233][24380] Starting process learner_proc0
14
+ [2023-03-17 10:54:05,282][24380] Starting all processes...
15
+ [2023-03-17 10:54:05,293][24380] Starting process inference_proc0-0
16
+ [2023-03-17 10:54:05,293][24380] Starting process rollout_proc0
17
+ [2023-03-17 10:54:05,294][24380] Starting process rollout_proc1
18
+ [2023-03-17 10:54:05,294][24380] Starting process rollout_proc2
19
+ [2023-03-17 10:54:05,294][24380] Starting process rollout_proc3
20
+ [2023-03-17 10:54:05,295][24380] Starting process rollout_proc4
21
+ [2023-03-17 10:54:05,295][24380] Starting process rollout_proc5
22
+ [2023-03-17 10:54:05,296][24380] Starting process rollout_proc6
23
+ [2023-03-17 10:54:05,297][24380] Starting process rollout_proc7
24
+ [2023-03-17 10:54:06,789][32549] Worker 0 uses CPU cores [0, 1]
25
+ [2023-03-17 10:54:06,820][32535] Using GPUs [0] for process 0 (actually maps to GPUs [0])
26
+ [2023-03-17 10:54:06,820][32535] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
27
+ [2023-03-17 10:54:06,833][32535] Num visible devices: 1
28
+ [2023-03-17 10:54:06,847][32568] Worker 1 uses CPU cores [2, 3]
29
+ [2023-03-17 10:54:06,860][32571] Worker 6 uses CPU cores [12, 13]
30
+ [2023-03-17 10:54:06,880][32572] Worker 7 uses CPU cores [14, 15]
31
+ [2023-03-17 10:54:06,892][32535] Starting seed is not provided
32
+ [2023-03-17 10:54:06,893][32535] Using GPUs [0] for process 0 (actually maps to GPUs [0])
33
+ [2023-03-17 10:54:06,893][32535] Initializing actor-critic model on device cuda:0
34
+ [2023-03-17 10:54:06,893][32535] RunningMeanStd input shape: (3, 72, 128)
35
+ [2023-03-17 10:54:06,894][32535] RunningMeanStd input shape: (1,)
36
+ [2023-03-17 10:54:06,904][32535] ConvEncoder: input_channels=3
37
+ [2023-03-17 10:54:06,995][32535] Conv encoder output size: 512
38
+ [2023-03-17 10:54:06,996][32535] Policy head output size: 512
39
+ [2023-03-17 10:54:07,006][32535] Created Actor Critic model with architecture:
40
+ [2023-03-17 10:54:07,006][32535] ActorCriticSharedWeights(
41
+ (obs_normalizer): ObservationNormalizer(
42
+ (running_mean_std): RunningMeanStdDictInPlace(
43
+ (running_mean_std): ModuleDict(
44
+ (obs): RunningMeanStdInPlace()
45
+ )
46
+ )
47
+ )
48
+ (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
49
+ (encoder): VizdoomEncoder(
50
+ (basic_encoder): ConvEncoder(
51
+ (enc): RecursiveScriptModule(
52
+ original_name=ConvEncoderImpl
53
+ (conv_head): RecursiveScriptModule(
54
+ original_name=Sequential
55
+ (0): RecursiveScriptModule(original_name=Conv2d)
56
+ (1): RecursiveScriptModule(original_name=ELU)
57
+ (2): RecursiveScriptModule(original_name=Conv2d)
58
+ (3): RecursiveScriptModule(original_name=ELU)
59
+ (4): RecursiveScriptModule(original_name=Conv2d)
60
+ (5): RecursiveScriptModule(original_name=ELU)
61
+ )
62
+ (mlp_layers): RecursiveScriptModule(
63
+ original_name=Sequential
64
+ (0): RecursiveScriptModule(original_name=Linear)
65
+ (1): RecursiveScriptModule(original_name=ELU)
66
+ )
67
+ )
68
+ )
69
+ )
70
+ (core): ModelCoreRNN(
71
+ (core): GRU(512, 512)
72
+ )
73
+ (decoder): MlpDecoder(
74
+ (mlp): Identity()
75
+ )
76
+ (critic_linear): Linear(in_features=512, out_features=1, bias=True)
77
+ (action_parameterization): ActionParameterizationDefault(
78
+ (distribution_linear): Linear(in_features=512, out_features=5, bias=True)
79
+ )
80
+ )
81
+ [2023-03-17 10:54:07,011][32570] Worker 5 uses CPU cores [10, 11]
82
+ [2023-03-17 10:54:07,020][32548] Using GPUs [0] for process 0 (actually maps to GPUs [0])
83
+ [2023-03-17 10:54:07,020][32548] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
84
+ [2023-03-17 10:54:07,035][32567] Worker 3 uses CPU cores [6, 7]
85
+ [2023-03-17 10:54:07,042][32548] Num visible devices: 1
86
+ [2023-03-17 10:54:07,165][32550] Worker 2 uses CPU cores [4, 5]
87
+ [2023-03-17 10:54:07,197][32569] Worker 4 uses CPU cores [8, 9]
88
+ [2023-03-17 10:54:08,475][32535] Using optimizer <class 'torch.optim.adam.Adam'>
89
+ [2023-03-17 10:54:08,476][32535] No checkpoints found
90
+ [2023-03-17 10:54:08,476][32535] Did not load from checkpoint, starting from scratch!
91
+ [2023-03-17 10:54:08,476][32535] Initialized policy 0 weights for model version 0
92
+ [2023-03-17 10:54:08,478][32535] LearnerWorker_p0 finished initialization!
93
+ [2023-03-17 10:54:08,478][32535] Using GPUs [0] for process 0 (actually maps to GPUs [0])
94
+ [2023-03-17 10:54:08,577][32548] RunningMeanStd input shape: (3, 72, 128)
95
+ [2023-03-17 10:54:08,578][32548] RunningMeanStd input shape: (1,)
96
+ [2023-03-17 10:54:08,586][32548] ConvEncoder: input_channels=3
97
+ [2023-03-17 10:54:08,657][32548] Conv encoder output size: 512
98
+ [2023-03-17 10:54:08,657][32548] Policy head output size: 512
99
+ [2023-03-17 10:54:10,077][24380] Inference worker 0-0 is ready!
100
+ [2023-03-17 10:54:10,079][24380] All inference workers are ready! Signal rollout workers to start!
101
+ [2023-03-17 10:54:10,106][32549] Doom resolution: 160x120, resize resolution: (128, 72)
102
+ [2023-03-17 10:54:10,106][32569] Doom resolution: 160x120, resize resolution: (128, 72)
103
+ [2023-03-17 10:54:10,111][32572] Doom resolution: 160x120, resize resolution: (128, 72)
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+ [2023-03-17 10:54:10,111][32550] Doom resolution: 160x120, resize resolution: (128, 72)
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+ [2023-03-17 10:54:10,111][32571] Doom resolution: 160x120, resize resolution: (128, 72)
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+ [2023-03-17 10:54:10,111][32567] Doom resolution: 160x120, resize resolution: (128, 72)
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+ [2023-03-17 10:54:10,112][32568] Doom resolution: 160x120, resize resolution: (128, 72)
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+ [2023-03-17 10:54:10,112][32570] Doom resolution: 160x120, resize resolution: (128, 72)
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+ [2023-03-17 10:54:10,368][32569] Decorrelating experience for 0 frames...
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+ [2023-03-17 10:54:10,368][32567] Decorrelating experience for 0 frames...
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+ [2023-03-17 10:54:10,368][32550] Decorrelating experience for 0 frames...
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+ [2023-03-17 10:54:10,369][32568] Decorrelating experience for 0 frames...
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+ [2023-03-17 10:54:10,395][32549] Decorrelating experience for 0 frames...
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+ [2023-03-17 10:54:10,580][32550] Decorrelating experience for 32 frames...
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+ [2023-03-17 10:54:10,586][32567] Decorrelating experience for 32 frames...
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+ [2023-03-17 10:54:10,592][32569] Decorrelating experience for 32 frames...
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+ [2023-03-17 10:54:10,862][32568] Decorrelating experience for 32 frames...
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+ [2023-03-17 10:54:10,879][32571] Decorrelating experience for 0 frames...
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+ [2023-03-17 10:54:10,880][32567] Decorrelating experience for 64 frames...
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+ [2023-03-17 10:54:10,921][32549] Decorrelating experience for 32 frames...
121
+ [2023-03-17 10:54:10,980][32569] Decorrelating experience for 64 frames...
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+ [2023-03-17 10:54:11,112][32572] Decorrelating experience for 0 frames...
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+ [2023-03-17 10:54:11,122][32571] Decorrelating experience for 32 frames...
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+ [2023-03-17 10:54:11,128][32568] Decorrelating experience for 64 frames...
125
+ [2023-03-17 10:54:11,154][32550] Decorrelating experience for 64 frames...
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+ [2023-03-17 10:54:11,215][32567] Decorrelating experience for 96 frames...
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+ [2023-03-17 10:54:11,347][32569] Decorrelating experience for 96 frames...
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+ [2023-03-17 10:54:11,396][32571] Decorrelating experience for 64 frames...
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+ [2023-03-17 10:54:11,428][32568] Decorrelating experience for 96 frames...
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+ [2023-03-17 10:54:11,430][32572] Decorrelating experience for 32 frames...
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+ [2023-03-17 10:54:11,475][32550] Decorrelating experience for 96 frames...
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+ [2023-03-17 10:54:11,640][32570] Decorrelating experience for 0 frames...
133
+ [2023-03-17 10:54:11,659][32549] Decorrelating experience for 64 frames...
134
+ [2023-03-17 10:54:11,690][32571] Decorrelating experience for 96 frames...
135
+ [2023-03-17 10:54:11,893][32570] Decorrelating experience for 32 frames...
136
+ [2023-03-17 10:54:11,952][32572] Decorrelating experience for 64 frames...
137
+ [2023-03-17 10:54:12,005][32549] Decorrelating experience for 96 frames...
138
+ [2023-03-17 10:54:12,218][32570] Decorrelating experience for 64 frames...
139
+ [2023-03-17 10:54:12,333][32572] Decorrelating experience for 96 frames...
140
+ [2023-03-17 10:54:12,422][32535] Signal inference workers to stop experience collection...
141
+ [2023-03-17 10:54:12,434][32548] InferenceWorker_p0-w0: stopping experience collection
142
+ [2023-03-17 10:54:12,554][32570] Decorrelating experience for 96 frames...
143
+ [2023-03-17 10:54:12,902][24380] 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)
144
+ [2023-03-17 10:54:12,904][24380] Avg episode reward: [(0, '2.711')]
145
+ [2023-03-17 10:54:13,106][32535] Signal inference workers to resume experience collection...
146
+ [2023-03-17 10:54:13,107][32548] InferenceWorker_p0-w0: resuming experience collection
147
+ [2023-03-17 10:54:15,446][32548] Updated weights for policy 0, policy_version 10 (0.0263)
148
+ [2023-03-17 10:54:17,635][32548] Updated weights for policy 0, policy_version 20 (0.0007)
149
+ [2023-03-17 10:54:17,902][24380] Fps is (10 sec: 17203.9, 60 sec: 17203.9, 300 sec: 17203.9). Total num frames: 86016. Throughput: 0: 2777.3. Samples: 13886. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
150
+ [2023-03-17 10:54:17,903][24380] Avg episode reward: [(0, '4.460')]
151
+ [2023-03-17 10:54:19,874][32548] Updated weights for policy 0, policy_version 30 (0.0008)
152
+ [2023-03-17 10:54:22,150][32548] Updated weights for policy 0, policy_version 40 (0.0008)
153
+ [2023-03-17 10:54:22,902][24380] Fps is (10 sec: 17612.5, 60 sec: 17612.5, 300 sec: 17612.5). Total num frames: 176128. Throughput: 0: 4135.1. Samples: 41352. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
154
+ [2023-03-17 10:54:22,904][24380] Avg episode reward: [(0, '4.468')]
155
+ [2023-03-17 10:54:22,912][32535] Saving new best policy, reward=4.468!
156
+ [2023-03-17 10:54:23,494][24380] Keyboard interrupt detected in the event loop EvtLoop [Runner_EvtLoop, process=main process 24380], exiting...
157
+ [2023-03-17 10:54:23,497][32535] Stopping Batcher_0...
158
+ [2023-03-17 10:54:23,498][32535] Loop batcher_evt_loop terminating...
159
+ [2023-03-17 10:54:23,497][24380] Runner profile tree view:
160
+ main_loop: 18.2648
161
+ [2023-03-17 10:54:23,499][32535] Saving /home/ckahmann/RL/train_dir/default_experiment/checkpoint_p0/checkpoint_000000045_184320.pth...
162
+ [2023-03-17 10:54:23,499][24380] Collected {0: 184320}, FPS: 10091.5
163
+ [2023-03-17 10:54:23,505][32549] Stopping RolloutWorker_w0...
164
+ [2023-03-17 10:54:23,506][32569] Stopping RolloutWorker_w4...
165
+ [2023-03-17 10:54:23,506][32549] Loop rollout_proc0_evt_loop terminating...
166
+ [2023-03-17 10:54:23,506][32569] Loop rollout_proc4_evt_loop terminating...
167
+ [2023-03-17 10:54:23,509][32570] Stopping RolloutWorker_w5...
168
+ [2023-03-17 10:54:23,509][32568] Stopping RolloutWorker_w1...
169
+ [2023-03-17 10:54:23,510][32570] Loop rollout_proc5_evt_loop terminating...
170
+ [2023-03-17 10:54:23,510][32568] Loop rollout_proc1_evt_loop terminating...
171
+ [2023-03-17 10:54:23,510][32548] Weights refcount: 2 0
172
+ [2023-03-17 10:54:23,511][32548] Stopping InferenceWorker_p0-w0...
173
+ [2023-03-17 10:54:23,512][32548] Loop inference_proc0-0_evt_loop terminating...
174
+ [2023-03-17 10:54:23,512][32550] Stopping RolloutWorker_w2...
175
+ [2023-03-17 10:54:23,513][32550] Loop rollout_proc2_evt_loop terminating...
176
+ [2023-03-17 10:54:23,515][32571] Stopping RolloutWorker_w6...
177
+ [2023-03-17 10:54:23,516][32571] Loop rollout_proc6_evt_loop terminating...
178
+ [2023-03-17 10:54:23,516][32567] Stopping RolloutWorker_w3...
179
+ [2023-03-17 10:54:23,517][32567] Loop rollout_proc3_evt_loop terminating...
180
+ [2023-03-17 10:54:23,525][32572] Stopping RolloutWorker_w7...
181
+ [2023-03-17 10:54:23,525][32572] Loop rollout_proc7_evt_loop terminating...
182
+ [2023-03-17 10:54:23,561][32535] Stopping LearnerWorker_p0...
183
+ [2023-03-17 10:54:23,562][32535] Loop learner_proc0_evt_loop terminating...
184
+ [2023-03-17 10:54:29,677][24380] Environment doom_basic already registered, overwriting...
185
+ [2023-03-17 10:54:29,680][24380] Environment doom_two_colors_easy already registered, overwriting...
186
+ [2023-03-17 10:54:29,682][24380] Environment doom_two_colors_hard already registered, overwriting...
187
+ [2023-03-17 10:54:29,684][24380] Environment doom_dm already registered, overwriting...
188
+ [2023-03-17 10:54:29,686][24380] Environment doom_dwango5 already registered, overwriting...
189
+ [2023-03-17 10:54:29,687][24380] Environment doom_my_way_home_flat_actions already registered, overwriting...
190
+ [2023-03-17 10:54:29,689][24380] Environment doom_defend_the_center_flat_actions already registered, overwriting...
191
+ [2023-03-17 10:54:29,690][24380] Environment doom_my_way_home already registered, overwriting...
192
+ [2023-03-17 10:54:29,692][24380] Environment doom_deadly_corridor already registered, overwriting...
193
+ [2023-03-17 10:54:29,693][24380] Environment doom_defend_the_center already registered, overwriting...
194
+ [2023-03-17 10:54:29,694][24380] Environment doom_defend_the_line already registered, overwriting...
195
+ [2023-03-17 10:54:29,695][24380] Environment doom_health_gathering already registered, overwriting...
196
+ [2023-03-17 10:54:29,695][24380] Environment doom_health_gathering_supreme already registered, overwriting...
197
+ [2023-03-17 10:54:29,696][24380] Environment doom_battle already registered, overwriting...
198
+ [2023-03-17 10:54:29,698][24380] Environment doom_battle2 already registered, overwriting...
199
+ [2023-03-17 10:54:29,699][24380] Environment doom_duel_bots already registered, overwriting...
200
+ [2023-03-17 10:54:29,699][24380] Environment doom_deathmatch_bots already registered, overwriting...
201
+ [2023-03-17 10:54:29,701][24380] Environment doom_duel already registered, overwriting...
202
+ [2023-03-17 10:54:29,701][24380] Environment doom_deathmatch_full already registered, overwriting...
203
+ [2023-03-17 10:54:29,702][24380] Environment doom_benchmark already registered, overwriting...
204
+ [2023-03-17 10:54:29,703][24380] register_encoder_factory: <function make_vizdoom_encoder at 0x7f2990bd61f0>
205
+ [2023-03-17 10:54:29,721][24380] Loading existing experiment configuration from /home/ckahmann/RL/train_dir/default_experiment/config.json
206
+ [2023-03-17 10:54:29,722][24380] Overriding arg 'num_workers' with value 16 passed from command line
207
+ [2023-03-17 10:54:29,729][24380] Experiment dir /home/ckahmann/RL/train_dir/default_experiment already exists!
208
+ [2023-03-17 10:54:29,730][24380] Resuming existing experiment from /home/ckahmann/RL/train_dir/default_experiment...
209
+ [2023-03-17 10:54:29,731][24380] Weights and Biases integration disabled
210
+ [2023-03-17 10:54:29,863][24380] Environment var CUDA_VISIBLE_DEVICES is 0,1
211
+
212
+ [2023-03-17 10:54:31,128][24380] Starting experiment with the following configuration:
213
+ help=False
214
+ algo=APPO
215
+ env=doom_health_gathering_supreme
216
+ experiment=default_experiment
217
+ train_dir=/home/ckahmann/RL/train_dir
218
+ restart_behavior=resume
219
+ device=gpu
220
+ seed=None
221
+ num_policies=1
222
+ async_rl=True
223
+ serial_mode=False
224
+ batched_sampling=False
225
+ num_batches_to_accumulate=2
226
+ worker_num_splits=2
227
+ policy_workers_per_policy=1
228
+ max_policy_lag=1000
229
+ num_workers=16
230
+ num_envs_per_worker=4
231
+ batch_size=1024
232
+ num_batches_per_epoch=1
233
+ num_epochs=1
234
+ rollout=32
235
+ recurrence=32
236
+ shuffle_minibatches=False
237
+ gamma=0.99
238
+ reward_scale=1.0
239
+ reward_clip=1000.0
240
+ value_bootstrap=False
241
+ normalize_returns=True
242
+ exploration_loss_coeff=0.001
243
+ value_loss_coeff=0.5
244
+ kl_loss_coeff=0.0
245
+ exploration_loss=symmetric_kl
246
+ gae_lambda=0.95
247
+ ppo_clip_ratio=0.1
248
+ ppo_clip_value=0.2
249
+ with_vtrace=False
250
+ vtrace_rho=1.0
251
+ vtrace_c=1.0
252
+ optimizer=adam
253
+ adam_eps=1e-06
254
+ adam_beta1=0.9
255
+ adam_beta2=0.999
256
+ max_grad_norm=4.0
257
+ learning_rate=0.0001
258
+ lr_schedule=constant
259
+ lr_schedule_kl_threshold=0.008
260
+ lr_adaptive_min=1e-06
261
+ lr_adaptive_max=0.01
262
+ obs_subtract_mean=0.0
263
+ obs_scale=255.0
264
+ normalize_input=True
265
+ normalize_input_keys=None
266
+ decorrelate_experience_max_seconds=0
267
+ decorrelate_envs_on_one_worker=True
268
+ actor_worker_gpus=[]
269
+ set_workers_cpu_affinity=True
270
+ force_envs_single_thread=False
271
+ default_niceness=0
272
+ log_to_file=True
273
+ experiment_summaries_interval=10
274
+ flush_summaries_interval=30
275
+ stats_avg=100
276
+ summaries_use_frameskip=True
277
+ heartbeat_interval=20
278
+ heartbeat_reporting_interval=600
279
+ train_for_env_steps=4000000
280
+ train_for_seconds=10000000000
281
+ save_every_sec=120
282
+ keep_checkpoints=2
283
+ load_checkpoint_kind=latest
284
+ save_milestones_sec=-1
285
+ save_best_every_sec=5
286
+ save_best_metric=reward
287
+ save_best_after=100000
288
+ benchmark=False
289
+ encoder_mlp_layers=[512, 512]
290
+ encoder_conv_architecture=convnet_simple
291
+ encoder_conv_mlp_layers=[512]
292
+ use_rnn=True
293
+ rnn_size=512
294
+ rnn_type=gru
295
+ rnn_num_layers=1
296
+ decoder_mlp_layers=[]
297
+ nonlinearity=elu
298
+ policy_initialization=orthogonal
299
+ policy_init_gain=1.0
300
+ actor_critic_share_weights=True
301
+ adaptive_stddev=True
302
+ continuous_tanh_scale=0.0
303
+ initial_stddev=1.0
304
+ use_env_info_cache=False
305
+ env_gpu_actions=False
306
+ env_gpu_observations=True
307
+ env_frameskip=4
308
+ env_framestack=1
309
+ pixel_format=CHW
310
+ use_record_episode_statistics=False
311
+ with_wandb=False
312
+ wandb_user=None
313
+ wandb_project=sample_factory
314
+ wandb_group=None
315
+ wandb_job_type=SF
316
+ wandb_tags=[]
317
+ with_pbt=False
318
+ pbt_mix_policies_in_one_env=True
319
+ pbt_period_env_steps=5000000
320
+ pbt_start_mutation=20000000
321
+ pbt_replace_fraction=0.3
322
+ pbt_mutation_rate=0.15
323
+ pbt_replace_reward_gap=0.1
324
+ pbt_replace_reward_gap_absolute=1e-06
325
+ pbt_optimize_gamma=False
326
+ pbt_target_objective=true_objective
327
+ pbt_perturb_min=1.1
328
+ pbt_perturb_max=1.5
329
+ num_agents=-1
330
+ num_humans=0
331
+ num_bots=-1
332
+ start_bot_difficulty=None
333
+ timelimit=None
334
+ res_w=128
335
+ res_h=72
336
+ wide_aspect_ratio=False
337
+ eval_env_frameskip=1
338
+ fps=35
339
+ command_line=--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000
340
+ cli_args={'env': 'doom_health_gathering_supreme', 'num_workers': 8, 'num_envs_per_worker': 4, 'train_for_env_steps': 4000000}
341
+ git_hash=unknown
342
+ git_repo_name=not a git repository
343
+ [2023-03-17 10:54:31,130][24380] Saving configuration to /home/ckahmann/RL/train_dir/default_experiment/config.json...
344
+ [2023-03-17 10:54:31,132][24380] Rollout worker 0 uses device cpu
345
+ [2023-03-17 10:54:31,132][24380] Rollout worker 1 uses device cpu
346
+ [2023-03-17 10:54:31,133][24380] Rollout worker 2 uses device cpu
347
+ [2023-03-17 10:54:31,134][24380] Rollout worker 3 uses device cpu
348
+ [2023-03-17 10:54:31,134][24380] Rollout worker 4 uses device cpu
349
+ [2023-03-17 10:54:31,135][24380] Rollout worker 5 uses device cpu
350
+ [2023-03-17 10:54:31,136][24380] Rollout worker 6 uses device cpu
351
+ [2023-03-17 10:54:31,137][24380] Rollout worker 7 uses device cpu
352
+ [2023-03-17 10:54:31,137][24380] Rollout worker 8 uses device cpu
353
+ [2023-03-17 10:54:31,138][24380] Rollout worker 9 uses device cpu
354
+ [2023-03-17 10:54:31,139][24380] Rollout worker 10 uses device cpu
355
+ [2023-03-17 10:54:31,139][24380] Rollout worker 11 uses device cpu
356
+ [2023-03-17 10:54:31,140][24380] Rollout worker 12 uses device cpu
357
+ [2023-03-17 10:54:31,141][24380] Rollout worker 13 uses device cpu
358
+ [2023-03-17 10:54:31,141][24380] Rollout worker 14 uses device cpu
359
+ [2023-03-17 10:54:31,142][24380] Rollout worker 15 uses device cpu
360
+ [2023-03-17 10:54:31,206][24380] Using GPUs [0] for process 0 (actually maps to GPUs [0])
361
+ [2023-03-17 10:54:31,207][24380] InferenceWorker_p0-w0: min num requests: 5
362
+ [2023-03-17 10:54:31,243][24380] Starting all processes...
363
+ [2023-03-17 10:54:31,244][24380] Starting process learner_proc0
364
+ [2023-03-17 10:54:31,297][24380] Starting all processes...
365
+ [2023-03-17 10:54:31,305][24380] Starting process inference_proc0-0
366
+ [2023-03-17 10:54:31,306][24380] Starting process rollout_proc0
367
+ [2023-03-17 10:54:31,306][24380] Starting process rollout_proc1
368
+ [2023-03-17 10:54:31,307][24380] Starting process rollout_proc2
369
+ [2023-03-17 10:54:31,307][24380] Starting process rollout_proc3
370
+ [2023-03-17 10:54:31,307][24380] Starting process rollout_proc4
371
+ [2023-03-17 10:54:31,308][24380] Starting process rollout_proc5
372
+ [2023-03-17 10:54:31,308][24380] Starting process rollout_proc6
373
+ [2023-03-17 10:54:31,309][24380] Starting process rollout_proc7
374
+ [2023-03-17 10:54:31,310][24380] Starting process rollout_proc8
375
+ [2023-03-17 10:54:31,310][24380] Starting process rollout_proc9
376
+ [2023-03-17 10:54:31,311][24380] Starting process rollout_proc10
377
+ [2023-03-17 10:54:31,312][24380] Starting process rollout_proc11
378
+ [2023-03-17 10:54:31,312][24380] Starting process rollout_proc12
379
+ [2023-03-17 10:54:31,318][24380] Starting process rollout_proc13
380
+ [2023-03-17 10:54:31,318][24380] Starting process rollout_proc14
381
+ [2023-03-17 10:54:31,378][24380] Starting process rollout_proc15
382
+ [2023-03-17 10:54:33,386][01277] Using GPUs [0] for process 0 (actually maps to GPUs [0])
383
+ [2023-03-17 10:54:33,386][01277] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
384
+ [2023-03-17 10:54:33,400][01277] Num visible devices: 1
385
+ [2023-03-17 10:54:33,448][01301] Worker 1 uses CPU cores [1]
386
+ [2023-03-17 10:54:33,448][01277] Starting seed is not provided
387
+ [2023-03-17 10:54:33,448][01277] Using GPUs [0] for process 0 (actually maps to GPUs [0])
388
+ [2023-03-17 10:54:33,449][01277] Initializing actor-critic model on device cuda:0
389
+ [2023-03-17 10:54:33,449][01277] RunningMeanStd input shape: (3, 72, 128)
390
+ [2023-03-17 10:54:33,450][01277] RunningMeanStd input shape: (1,)
391
+ [2023-03-17 10:54:33,466][01277] ConvEncoder: input_channels=3
392
+ [2023-03-17 10:54:33,524][01299] Using GPUs [0] for process 0 (actually maps to GPUs [0])
393
+ [2023-03-17 10:54:33,524][01299] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
394
+ [2023-03-17 10:54:33,536][01324] Worker 6 uses CPU cores [6]
395
+ [2023-03-17 10:54:33,550][01299] Num visible devices: 1
396
+ [2023-03-17 10:54:33,552][01325] Worker 8 uses CPU cores [8]
397
+ [2023-03-17 10:54:33,580][01300] Worker 0 uses CPU cores [0]
398
+ [2023-03-17 10:54:33,596][01323] Worker 5 uses CPU cores [5]
399
+ [2023-03-17 10:54:33,604][01321] Worker 4 uses CPU cores [4]
400
+ [2023-03-17 10:54:33,648][01320] Worker 3 uses CPU cores [3]
401
+ [2023-03-17 10:54:33,676][01328] Worker 10 uses CPU cores [10]
402
+ [2023-03-17 10:54:33,685][01277] Conv encoder output size: 512
403
+ [2023-03-17 10:54:33,686][01277] Policy head output size: 512
404
+ [2023-03-17 10:54:33,696][01326] Worker 7 uses CPU cores [7]
405
+ [2023-03-17 10:54:33,704][01277] Created Actor Critic model with architecture:
406
+ [2023-03-17 10:54:33,704][01277] ActorCriticSharedWeights(
407
+ (obs_normalizer): ObservationNormalizer(
408
+ (running_mean_std): RunningMeanStdDictInPlace(
409
+ (running_mean_std): ModuleDict(
410
+ (obs): RunningMeanStdInPlace()
411
+ )
412
+ )
413
+ )
414
+ (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
415
+ (encoder): VizdoomEncoder(
416
+ (basic_encoder): ConvEncoder(
417
+ (enc): RecursiveScriptModule(
418
+ original_name=ConvEncoderImpl
419
+ (conv_head): RecursiveScriptModule(
420
+ original_name=Sequential
421
+ (0): RecursiveScriptModule(original_name=Conv2d)
422
+ (1): RecursiveScriptModule(original_name=ELU)
423
+ (2): RecursiveScriptModule(original_name=Conv2d)
424
+ (3): RecursiveScriptModule(original_name=ELU)
425
+ (4): RecursiveScriptModule(original_name=Conv2d)
426
+ (5): RecursiveScriptModule(original_name=ELU)
427
+ )
428
+ (mlp_layers): RecursiveScriptModule(
429
+ original_name=Sequential
430
+ (0): RecursiveScriptModule(original_name=Linear)
431
+ (1): RecursiveScriptModule(original_name=ELU)
432
+ )
433
+ )
434
+ )
435
+ )
436
+ (core): ModelCoreRNN(
437
+ (core): GRU(512, 512)
438
+ )
439
+ (decoder): MlpDecoder(
440
+ (mlp): Identity()
441
+ )
442
+ (critic_linear): Linear(in_features=512, out_features=1, bias=True)
443
+ (action_parameterization): ActionParameterizationDefault(
444
+ (distribution_linear): Linear(in_features=512, out_features=5, bias=True)
445
+ )
446
+ )
447
+ [2023-03-17 10:54:33,752][01319] Worker 2 uses CPU cores [2]
448
+ [2023-03-17 10:54:33,775][01329] Worker 11 uses CPU cores [11]
449
+ [2023-03-17 10:54:33,824][01335] Worker 13 uses CPU cores [13]
450
+ [2023-03-17 10:54:33,840][01332] Worker 15 uses CPU cores [15]
451
+ [2023-03-17 10:54:33,876][01327] Worker 9 uses CPU cores [9]
452
+ [2023-03-17 10:54:33,990][01331] Worker 12 uses CPU cores [12]
453
+ [2023-03-17 10:54:33,993][01334] Worker 14 uses CPU cores [14]
454
+ [2023-03-17 10:54:35,256][01277] Using optimizer <class 'torch.optim.adam.Adam'>
455
+ [2023-03-17 10:54:35,257][01277] Loading state from checkpoint /home/ckahmann/RL/train_dir/default_experiment/checkpoint_p0/checkpoint_000000045_184320.pth...
456
+ [2023-03-17 10:54:35,278][01277] Loading model from checkpoint
457
+ [2023-03-17 10:54:35,281][01277] Loaded experiment state at self.train_step=45, self.env_steps=184320
458
+ [2023-03-17 10:54:35,281][01277] Initialized policy 0 weights for model version 45
459
+ [2023-03-17 10:54:35,283][01277] LearnerWorker_p0 finished initialization!
460
+ [2023-03-17 10:54:35,283][01277] Using GPUs [0] for process 0 (actually maps to GPUs [0])
461
+ [2023-03-17 10:54:35,387][01299] RunningMeanStd input shape: (3, 72, 128)
462
+ [2023-03-17 10:54:35,388][01299] RunningMeanStd input shape: (1,)
463
+ [2023-03-17 10:54:35,396][01299] ConvEncoder: input_channels=3
464
+ [2023-03-17 10:54:35,467][01299] Conv encoder output size: 512
465
+ [2023-03-17 10:54:35,467][01299] Policy head output size: 512
466
+ [2023-03-17 10:54:36,963][24380] Inference worker 0-0 is ready!
467
+ [2023-03-17 10:54:36,965][24380] All inference workers are ready! Signal rollout workers to start!
468
+ [2023-03-17 10:54:36,987][01301] Doom resolution: 160x120, resize resolution: (128, 72)
469
+ [2023-03-17 10:54:36,987][01327] Doom resolution: 160x120, resize resolution: (128, 72)
470
+ [2023-03-17 10:54:36,998][01323] Doom resolution: 160x120, resize resolution: (128, 72)
471
+ [2023-03-17 10:54:36,998][01334] Doom resolution: 160x120, resize resolution: (128, 72)
472
+ [2023-03-17 10:54:36,998][01335] Doom resolution: 160x120, resize resolution: (128, 72)
473
+ [2023-03-17 10:54:36,998][01324] Doom resolution: 160x120, resize resolution: (128, 72)
474
+ [2023-03-17 10:54:36,999][01326] Doom resolution: 160x120, resize resolution: (128, 72)
475
+ [2023-03-17 10:54:36,999][01320] Doom resolution: 160x120, resize resolution: (128, 72)
476
+ [2023-03-17 10:54:37,000][01329] Doom resolution: 160x120, resize resolution: (128, 72)
477
+ [2023-03-17 10:54:37,000][01319] Doom resolution: 160x120, resize resolution: (128, 72)
478
+ [2023-03-17 10:54:37,001][01321] Doom resolution: 160x120, resize resolution: (128, 72)
479
+ [2023-03-17 10:54:37,001][01331] Doom resolution: 160x120, resize resolution: (128, 72)
480
+ [2023-03-17 10:54:37,001][01328] Doom resolution: 160x120, resize resolution: (128, 72)
481
+ [2023-03-17 10:54:37,001][01300] Doom resolution: 160x120, resize resolution: (128, 72)
482
+ [2023-03-17 10:54:37,002][01325] Doom resolution: 160x120, resize resolution: (128, 72)
483
+ [2023-03-17 10:54:37,005][01332] Doom resolution: 160x120, resize resolution: (128, 72)
484
+ [2023-03-17 10:54:37,233][01324] Decorrelating experience for 0 frames...
485
+ [2023-03-17 10:54:37,234][01319] Decorrelating experience for 0 frames...
486
+ [2023-03-17 10:54:37,235][01326] Decorrelating experience for 0 frames...
487
+ [2023-03-17 10:54:37,340][01301] Decorrelating experience for 0 frames...
488
+ [2023-03-17 10:54:37,340][01327] Decorrelating experience for 0 frames...
489
+ [2023-03-17 10:54:37,437][01319] Decorrelating experience for 32 frames...
490
+ [2023-03-17 10:54:37,470][01324] Decorrelating experience for 32 frames...
491
+ [2023-03-17 10:54:37,489][01335] Decorrelating experience for 0 frames...
492
+ [2023-03-17 10:54:37,496][01320] Decorrelating experience for 0 frames...
493
+ [2023-03-17 10:54:37,496][01321] Decorrelating experience for 0 frames...
494
+ [2023-03-17 10:54:37,544][01301] Decorrelating experience for 32 frames...
495
+ [2023-03-17 10:54:37,631][01334] Decorrelating experience for 0 frames...
496
+ [2023-03-17 10:54:37,692][01335] Decorrelating experience for 32 frames...
497
+ [2023-03-17 10:54:37,698][01321] Decorrelating experience for 32 frames...
498
+ [2023-03-17 10:54:37,717][01326] Decorrelating experience for 32 frames...
499
+ [2023-03-17 10:54:37,764][01327] Decorrelating experience for 32 frames...
500
+ [2023-03-17 10:54:37,820][01332] Decorrelating experience for 0 frames...
501
+ [2023-03-17 10:54:37,851][01325] Decorrelating experience for 0 frames...
502
+ [2023-03-17 10:54:37,929][01335] Decorrelating experience for 64 frames...
503
+ [2023-03-17 10:54:37,948][01328] Decorrelating experience for 0 frames...
504
+ [2023-03-17 10:54:37,967][01320] Decorrelating experience for 32 frames...
505
+ [2023-03-17 10:54:38,061][01301] Decorrelating experience for 64 frames...
506
+ [2023-03-17 10:54:38,067][01334] Decorrelating experience for 32 frames...
507
+ [2023-03-17 10:54:38,153][01321] Decorrelating experience for 64 frames...
508
+ [2023-03-17 10:54:38,164][01324] Decorrelating experience for 64 frames...
509
+ [2023-03-17 10:54:38,252][01326] Decorrelating experience for 64 frames...
510
+ [2023-03-17 10:54:38,327][01320] Decorrelating experience for 64 frames...
511
+ [2023-03-17 10:54:38,354][01332] Decorrelating experience for 32 frames...
512
+ [2023-03-17 10:54:38,378][01321] Decorrelating experience for 96 frames...
513
+ [2023-03-17 10:54:38,395][01319] Decorrelating experience for 64 frames...
514
+ [2023-03-17 10:54:38,447][01324] Decorrelating experience for 96 frames...
515
+ [2023-03-17 10:54:38,560][01334] Decorrelating experience for 64 frames...
516
+ [2023-03-17 10:54:38,571][01327] Decorrelating experience for 64 frames...
517
+ [2023-03-17 10:54:38,653][01335] Decorrelating experience for 96 frames...
518
+ [2023-03-17 10:54:38,654][01329] Decorrelating experience for 0 frames...
519
+ [2023-03-17 10:54:38,705][01332] Decorrelating experience for 64 frames...
520
+ [2023-03-17 10:54:38,716][01326] Decorrelating experience for 96 frames...
521
+ [2023-03-17 10:54:38,797][01301] Decorrelating experience for 96 frames...
522
+ [2023-03-17 10:54:38,805][01331] Decorrelating experience for 0 frames...
523
+ [2023-03-17 10:54:38,913][01300] Decorrelating experience for 0 frames...
524
+ [2023-03-17 10:54:38,982][01334] Decorrelating experience for 96 frames...
525
+ [2023-03-17 10:54:38,992][01332] Decorrelating experience for 96 frames...
526
+ [2023-03-17 10:54:39,016][01319] Decorrelating experience for 96 frames...
527
+ [2023-03-17 10:54:39,059][01331] Decorrelating experience for 32 frames...
528
+ [2023-03-17 10:54:39,112][01328] Decorrelating experience for 32 frames...
529
+ [2023-03-17 10:54:39,244][01323] Decorrelating experience for 0 frames...
530
+ [2023-03-17 10:54:39,278][01300] Decorrelating experience for 32 frames...
531
+ [2023-03-17 10:54:39,359][01329] Decorrelating experience for 32 frames...
532
+ [2023-03-17 10:54:39,411][01320] Decorrelating experience for 96 frames...
533
+ [2023-03-17 10:54:39,431][01331] Decorrelating experience for 64 frames...
534
+ [2023-03-17 10:54:39,459][01325] Decorrelating experience for 32 frames...
535
+ [2023-03-17 10:54:39,647][01300] Decorrelating experience for 64 frames...
536
+ [2023-03-17 10:54:39,650][01277] Signal inference workers to stop experience collection...
537
+ [2023-03-17 10:54:39,654][01299] InferenceWorker_p0-w0: stopping experience collection
538
+ [2023-03-17 10:54:39,690][01323] Decorrelating experience for 32 frames...
539
+ [2023-03-17 10:54:39,715][01327] Decorrelating experience for 96 frames...
540
+ [2023-03-17 10:54:39,718][01328] Decorrelating experience for 64 frames...
541
+ [2023-03-17 10:54:39,734][24380] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 184320. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
542
+ [2023-03-17 10:54:39,736][24380] Avg episode reward: [(0, '2.928')]
543
+ [2023-03-17 10:54:39,808][01325] Decorrelating experience for 64 frames...
544
+ [2023-03-17 10:54:39,927][01331] Decorrelating experience for 96 frames...
545
+ [2023-03-17 10:54:39,930][01323] Decorrelating experience for 64 frames...
546
+ [2023-03-17 10:54:39,947][01329] Decorrelating experience for 64 frames...
547
+ [2023-03-17 10:54:40,043][01300] Decorrelating experience for 96 frames...
548
+ [2023-03-17 10:54:40,080][01328] Decorrelating experience for 96 frames...
549
+ [2023-03-17 10:54:40,145][01325] Decorrelating experience for 96 frames...
550
+ [2023-03-17 10:54:40,156][01323] Decorrelating experience for 96 frames...
551
+ [2023-03-17 10:54:40,287][01329] Decorrelating experience for 96 frames...
552
+ [2023-03-17 10:54:40,479][01277] Signal inference workers to resume experience collection...
553
+ [2023-03-17 10:54:40,480][01299] InferenceWorker_p0-w0: resuming experience collection
554
+ [2023-03-17 10:54:42,279][01299] Updated weights for policy 0, policy_version 55 (0.0267)
555
+ [2023-03-17 10:54:43,568][01299] Updated weights for policy 0, policy_version 65 (0.0010)
556
+ [2023-03-17 10:54:44,734][24380] Fps is (10 sec: 23757.4, 60 sec: 23757.4, 300 sec: 23757.4). Total num frames: 303104. Throughput: 0: 3473.3. Samples: 17366. Policy #0 lag: (min: 0.0, avg: 1.3, max: 3.0)
557
+ [2023-03-17 10:54:44,735][24380] Avg episode reward: [(0, '4.797')]
558
+ [2023-03-17 10:54:44,843][01299] Updated weights for policy 0, policy_version 75 (0.0011)
559
+ [2023-03-17 10:54:44,884][01277] Saving new best policy, reward=4.827!
560
+ [2023-03-17 10:54:46,123][01299] Updated weights for policy 0, policy_version 85 (0.0010)
561
+ [2023-03-17 10:54:47,363][01299] Updated weights for policy 0, policy_version 95 (0.0012)
562
+ [2023-03-17 10:54:48,642][01299] Updated weights for policy 0, policy_version 105 (0.0010)
563
+ [2023-03-17 10:54:49,734][24380] Fps is (10 sec: 27853.3, 60 sec: 27853.3, 300 sec: 27853.3). Total num frames: 462848. Throughput: 0: 6522.7. Samples: 65226. Policy #0 lag: (min: 0.0, avg: 1.3, max: 3.0)
564
+ [2023-03-17 10:54:49,735][24380] Avg episode reward: [(0, '4.902')]
565
+ [2023-03-17 10:54:49,920][01299] Updated weights for policy 0, policy_version 115 (0.0014)
566
+ [2023-03-17 10:54:51,201][01299] Updated weights for policy 0, policy_version 125 (0.0012)
567
+ [2023-03-17 10:54:51,201][24380] Heartbeat connected on Batcher_0
568
+ [2023-03-17 10:54:51,203][24380] Heartbeat connected on LearnerWorker_p0
569
+ [2023-03-17 10:54:51,211][24380] Heartbeat connected on InferenceWorker_p0-w0
570
+ [2023-03-17 10:54:51,212][24380] Heartbeat connected on RolloutWorker_w0
571
+ [2023-03-17 10:54:51,213][24380] Heartbeat connected on RolloutWorker_w1
572
+ [2023-03-17 10:54:51,219][24380] Heartbeat connected on RolloutWorker_w3
573
+ [2023-03-17 10:54:51,220][24380] Heartbeat connected on RolloutWorker_w2
574
+ [2023-03-17 10:54:51,222][24380] Heartbeat connected on RolloutWorker_w4
575
+ [2023-03-17 10:54:51,223][24380] Heartbeat connected on RolloutWorker_w5
576
+ [2023-03-17 10:54:51,225][24380] Heartbeat connected on RolloutWorker_w6
577
+ [2023-03-17 10:54:51,226][24380] Heartbeat connected on RolloutWorker_w7
578
+ [2023-03-17 10:54:51,232][24380] Heartbeat connected on RolloutWorker_w9
579
+ [2023-03-17 10:54:51,233][24380] Heartbeat connected on RolloutWorker_w8
580
+ [2023-03-17 10:54:51,234][24380] Heartbeat connected on RolloutWorker_w10
581
+ [2023-03-17 10:54:51,238][24380] Heartbeat connected on RolloutWorker_w12
582
+ [2023-03-17 10:54:51,239][24380] Heartbeat connected on RolloutWorker_w13
583
+ [2023-03-17 10:54:51,241][24380] Heartbeat connected on RolloutWorker_w11
584
+ [2023-03-17 10:54:51,241][24380] Heartbeat connected on RolloutWorker_w14
585
+ [2023-03-17 10:54:51,245][24380] Heartbeat connected on RolloutWorker_w15
586
+ [2023-03-17 10:54:52,537][01299] Updated weights for policy 0, policy_version 135 (0.0014)
587
+ [2023-03-17 10:54:53,836][01299] Updated weights for policy 0, policy_version 145 (0.0010)
588
+ [2023-03-17 10:54:54,734][24380] Fps is (10 sec: 31949.1, 60 sec: 29218.5, 300 sec: 29218.5). Total num frames: 622592. Throughput: 0: 5924.1. Samples: 88860. Policy #0 lag: (min: 0.0, avg: 1.2, max: 2.0)
589
+ [2023-03-17 10:54:54,735][24380] Avg episode reward: [(0, '5.518')]
590
+ [2023-03-17 10:54:54,881][01277] Saving new best policy, reward=5.635!
591
+ [2023-03-17 10:54:55,142][01299] Updated weights for policy 0, policy_version 155 (0.0012)
592
+ [2023-03-17 10:54:56,424][01299] Updated weights for policy 0, policy_version 165 (0.0010)
593
+ [2023-03-17 10:54:57,687][01299] Updated weights for policy 0, policy_version 175 (0.0015)
594
+ [2023-03-17 10:54:58,936][01299] Updated weights for policy 0, policy_version 185 (0.0009)
595
+ [2023-03-17 10:54:59,734][24380] Fps is (10 sec: 31948.6, 60 sec: 29901.0, 300 sec: 29901.0). Total num frames: 782336. Throughput: 0: 6841.6. Samples: 136832. Policy #0 lag: (min: 0.0, avg: 1.2, max: 3.0)
596
+ [2023-03-17 10:54:59,735][24380] Avg episode reward: [(0, '6.663')]
597
+ [2023-03-17 10:54:59,864][01277] Saving new best policy, reward=6.669!
598
+ [2023-03-17 10:55:00,226][01299] Updated weights for policy 0, policy_version 195 (0.0011)
599
+ [2023-03-17 10:55:01,473][01299] Updated weights for policy 0, policy_version 205 (0.0011)
600
+ [2023-03-17 10:55:02,736][01299] Updated weights for policy 0, policy_version 215 (0.0010)
601
+ [2023-03-17 10:55:04,018][01299] Updated weights for policy 0, policy_version 225 (0.0013)
602
+ [2023-03-17 10:55:04,734][24380] Fps is (10 sec: 31948.6, 60 sec: 30310.6, 300 sec: 30310.6). Total num frames: 942080. Throughput: 0: 7402.4. Samples: 185058. Policy #0 lag: (min: 0.0, avg: 1.3, max: 3.0)
603
+ [2023-03-17 10:55:04,735][24380] Avg episode reward: [(0, '7.065')]
604
+ [2023-03-17 10:55:04,863][01277] Saving new best policy, reward=7.036!
605
+ [2023-03-17 10:55:05,319][01299] Updated weights for policy 0, policy_version 235 (0.0009)
606
+ [2023-03-17 10:55:06,589][01299] Updated weights for policy 0, policy_version 245 (0.0010)
607
+ [2023-03-17 10:55:07,846][01299] Updated weights for policy 0, policy_version 255 (0.0011)
608
+ [2023-03-17 10:55:09,162][01299] Updated weights for policy 0, policy_version 265 (0.0010)
609
+ [2023-03-17 10:55:09,734][24380] Fps is (10 sec: 31948.6, 60 sec: 30583.5, 300 sec: 30583.5). Total num frames: 1101824. Throughput: 0: 6968.3. Samples: 209050. Policy #0 lag: (min: 0.0, avg: 1.3, max: 3.0)
610
+ [2023-03-17 10:55:09,736][24380] Avg episode reward: [(0, '8.389')]
611
+ [2023-03-17 10:55:09,863][01277] Saving new best policy, reward=8.525!
612
+ [2023-03-17 10:55:10,439][01299] Updated weights for policy 0, policy_version 275 (0.0014)
613
+ [2023-03-17 10:55:11,734][01299] Updated weights for policy 0, policy_version 285 (0.0010)
614
+ [2023-03-17 10:55:13,008][01299] Updated weights for policy 0, policy_version 295 (0.0014)
615
+ [2023-03-17 10:55:14,302][01299] Updated weights for policy 0, policy_version 305 (0.0012)
616
+ [2023-03-17 10:55:14,734][24380] Fps is (10 sec: 31948.7, 60 sec: 30778.6, 300 sec: 30778.6). Total num frames: 1261568. Throughput: 0: 7341.9. Samples: 256964. Policy #0 lag: (min: 0.0, avg: 1.3, max: 3.0)
617
+ [2023-03-17 10:55:14,735][24380] Avg episode reward: [(0, '10.328')]
618
+ [2023-03-17 10:55:14,863][01277] Saving new best policy, reward=10.279!
619
+ [2023-03-17 10:55:15,612][01299] Updated weights for policy 0, policy_version 315 (0.0011)
620
+ [2023-03-17 10:55:16,888][01299] Updated weights for policy 0, policy_version 325 (0.0013)
621
+ [2023-03-17 10:55:18,147][01299] Updated weights for policy 0, policy_version 335 (0.0010)
622
+ [2023-03-17 10:55:19,395][01299] Updated weights for policy 0, policy_version 345 (0.0015)
623
+ [2023-03-17 10:55:19,734][24380] Fps is (10 sec: 31949.0, 60 sec: 30924.9, 300 sec: 30924.9). Total num frames: 1421312. Throughput: 0: 7627.8. Samples: 305112. Policy #0 lag: (min: 0.0, avg: 1.2, max: 3.0)
624
+ [2023-03-17 10:55:19,735][24380] Avg episode reward: [(0, '12.270')]
625
+ [2023-03-17 10:55:19,863][01277] Saving new best policy, reward=12.489!
626
+ [2023-03-17 10:55:20,693][01299] Updated weights for policy 0, policy_version 355 (0.0009)
627
+ [2023-03-17 10:55:21,936][01299] Updated weights for policy 0, policy_version 365 (0.0013)
628
+ [2023-03-17 10:55:23,234][01299] Updated weights for policy 0, policy_version 375 (0.0016)
629
+ [2023-03-17 10:55:24,533][01299] Updated weights for policy 0, policy_version 385 (0.0013)
630
+ [2023-03-17 10:55:24,735][24380] Fps is (10 sec: 31947.2, 60 sec: 31038.3, 300 sec: 31038.3). Total num frames: 1581056. Throughput: 0: 7309.9. Samples: 328948. Policy #0 lag: (min: 0.0, avg: 1.1, max: 2.0)
631
+ [2023-03-17 10:55:24,736][24380] Avg episode reward: [(0, '12.939')]
632
+ [2023-03-17 10:55:24,863][01277] Saving new best policy, reward=13.697!
633
+ [2023-03-17 10:55:25,807][01299] Updated weights for policy 0, policy_version 395 (0.0011)
634
+ [2023-03-17 10:55:27,094][01299] Updated weights for policy 0, policy_version 405 (0.0013)
635
+ [2023-03-17 10:55:28,354][01299] Updated weights for policy 0, policy_version 415 (0.0010)
636
+ [2023-03-17 10:55:29,639][01299] Updated weights for policy 0, policy_version 425 (0.0011)
637
+ [2023-03-17 10:55:29,734][24380] Fps is (10 sec: 31949.0, 60 sec: 31129.7, 300 sec: 31129.7). Total num frames: 1740800. Throughput: 0: 7994.3. Samples: 377110. Policy #0 lag: (min: 0.0, avg: 1.2, max: 3.0)
638
+ [2023-03-17 10:55:29,735][24380] Avg episode reward: [(0, '13.684')]
639
+ [2023-03-17 10:55:30,934][01299] Updated weights for policy 0, policy_version 435 (0.0016)
640
+ [2023-03-17 10:55:32,233][01299] Updated weights for policy 0, policy_version 445 (0.0011)
641
+ [2023-03-17 10:55:33,489][01299] Updated weights for policy 0, policy_version 455 (0.0011)
642
+ [2023-03-17 10:55:34,734][24380] Fps is (10 sec: 31950.5, 60 sec: 31204.2, 300 sec: 31204.2). Total num frames: 1900544. Throughput: 0: 8000.2. Samples: 425234. Policy #0 lag: (min: 0.0, avg: 1.4, max: 3.0)
643
+ [2023-03-17 10:55:34,735][24380] Avg episode reward: [(0, '16.289')]
644
+ [2023-03-17 10:55:34,772][01299] Updated weights for policy 0, policy_version 465 (0.0012)
645
+ [2023-03-17 10:55:34,863][01277] Saving new best policy, reward=16.870!
646
+ [2023-03-17 10:55:36,008][01299] Updated weights for policy 0, policy_version 475 (0.0013)
647
+ [2023-03-17 10:55:37,281][01299] Updated weights for policy 0, policy_version 485 (0.0009)
648
+ [2023-03-17 10:55:38,525][01299] Updated weights for policy 0, policy_version 495 (0.0011)
649
+ [2023-03-17 10:55:39,734][24380] Fps is (10 sec: 32358.4, 60 sec: 31334.5, 300 sec: 31334.5). Total num frames: 2064384. Throughput: 0: 8013.5. Samples: 449468. Policy #0 lag: (min: 0.0, avg: 1.1, max: 3.0)
650
+ [2023-03-17 10:55:39,735][24380] Avg episode reward: [(0, '18.133')]
651
+ [2023-03-17 10:55:39,804][01299] Updated weights for policy 0, policy_version 505 (0.0010)
652
+ [2023-03-17 10:55:39,863][01277] Saving new best policy, reward=17.945!
653
+ [2023-03-17 10:55:41,090][01299] Updated weights for policy 0, policy_version 515 (0.0010)
654
+ [2023-03-17 10:55:42,364][01299] Updated weights for policy 0, policy_version 525 (0.0011)
655
+ [2023-03-17 10:55:43,627][01299] Updated weights for policy 0, policy_version 535 (0.0012)
656
+ [2023-03-17 10:55:44,734][24380] Fps is (10 sec: 32358.4, 60 sec: 32017.1, 300 sec: 31381.7). Total num frames: 2224128. Throughput: 0: 8018.6. Samples: 497668. Policy #0 lag: (min: 0.0, avg: 1.3, max: 3.0)
657
+ [2023-03-17 10:55:44,735][24380] Avg episode reward: [(0, '17.323')]
658
+ [2023-03-17 10:55:44,892][01299] Updated weights for policy 0, policy_version 545 (0.0012)
659
+ [2023-03-17 10:55:46,183][01299] Updated weights for policy 0, policy_version 555 (0.0011)
660
+ [2023-03-17 10:55:47,442][01299] Updated weights for policy 0, policy_version 565 (0.0015)
661
+ [2023-03-17 10:55:48,670][01299] Updated weights for policy 0, policy_version 575 (0.0009)
662
+ [2023-03-17 10:55:49,734][24380] Fps is (10 sec: 32358.2, 60 sec: 32085.3, 300 sec: 31480.7). Total num frames: 2387968. Throughput: 0: 8031.5. Samples: 546474. Policy #0 lag: (min: 0.0, avg: 1.1, max: 3.0)
663
+ [2023-03-17 10:55:49,735][24380] Avg episode reward: [(0, '18.752')]
664
+ [2023-03-17 10:55:49,863][01277] Saving new best policy, reward=18.918!
665
+ [2023-03-17 10:55:49,969][01299] Updated weights for policy 0, policy_version 585 (0.0011)
666
+ [2023-03-17 10:55:51,171][01299] Updated weights for policy 0, policy_version 595 (0.0010)
667
+ [2023-03-17 10:55:52,434][01299] Updated weights for policy 0, policy_version 605 (0.0009)
668
+ [2023-03-17 10:55:53,704][01299] Updated weights for policy 0, policy_version 615 (0.0012)
669
+ [2023-03-17 10:55:54,734][24380] Fps is (10 sec: 32767.9, 60 sec: 32153.6, 300 sec: 31566.6). Total num frames: 2551808. Throughput: 0: 8037.4. Samples: 570730. Policy #0 lag: (min: 0.0, avg: 1.2, max: 3.0)
670
+ [2023-03-17 10:55:54,735][24380] Avg episode reward: [(0, '17.902')]
671
+ [2023-03-17 10:55:54,984][01299] Updated weights for policy 0, policy_version 625 (0.0010)
672
+ [2023-03-17 10:55:56,291][01299] Updated weights for policy 0, policy_version 635 (0.0012)
673
+ [2023-03-17 10:55:57,564][01299] Updated weights for policy 0, policy_version 645 (0.0013)
674
+ [2023-03-17 10:55:58,843][01299] Updated weights for policy 0, policy_version 655 (0.0014)
675
+ [2023-03-17 10:55:59,734][24380] Fps is (10 sec: 31949.0, 60 sec: 32085.4, 300 sec: 31539.3). Total num frames: 2707456. Throughput: 0: 8041.9. Samples: 618848. Policy #0 lag: (min: 0.0, avg: 1.2, max: 3.0)
676
+ [2023-03-17 10:55:59,735][24380] Avg episode reward: [(0, '19.811')]
677
+ [2023-03-17 10:55:59,863][01277] Saving new best policy, reward=19.975!
678
+ [2023-03-17 10:56:00,116][01299] Updated weights for policy 0, policy_version 665 (0.0015)
679
+ [2023-03-17 10:56:01,390][01299] Updated weights for policy 0, policy_version 675 (0.0010)
680
+ [2023-03-17 10:56:02,654][01299] Updated weights for policy 0, policy_version 685 (0.0012)
681
+ [2023-03-17 10:56:03,932][01299] Updated weights for policy 0, policy_version 695 (0.0012)
682
+ [2023-03-17 10:56:04,734][24380] Fps is (10 sec: 31949.0, 60 sec: 32153.6, 300 sec: 31611.6). Total num frames: 2871296. Throughput: 0: 8045.4. Samples: 667156. Policy #0 lag: (min: 0.0, avg: 1.2, max: 3.0)
683
+ [2023-03-17 10:56:04,735][24380] Avg episode reward: [(0, '20.363')]
684
+ [2023-03-17 10:56:04,863][01277] Saving new best policy, reward=20.373!
685
+ [2023-03-17 10:56:05,206][01299] Updated weights for policy 0, policy_version 705 (0.0009)
686
+ [2023-03-17 10:56:06,462][01299] Updated weights for policy 0, policy_version 715 (0.0012)
687
+ [2023-03-17 10:56:07,751][01299] Updated weights for policy 0, policy_version 725 (0.0013)
688
+ [2023-03-17 10:56:09,058][01299] Updated weights for policy 0, policy_version 735 (0.0011)
689
+ [2023-03-17 10:56:09,734][24380] Fps is (10 sec: 32358.4, 60 sec: 32153.7, 300 sec: 31630.3). Total num frames: 3031040. Throughput: 0: 8050.5. Samples: 691216. Policy #0 lag: (min: 0.0, avg: 1.2, max: 3.0)
690
+ [2023-03-17 10:56:09,735][24380] Avg episode reward: [(0, '22.248')]
691
+ [2023-03-17 10:56:09,863][01277] Saving new best policy, reward=22.610!
692
+ [2023-03-17 10:56:10,354][01299] Updated weights for policy 0, policy_version 745 (0.0014)
693
+ [2023-03-17 10:56:11,597][01299] Updated weights for policy 0, policy_version 755 (0.0009)
694
+ [2023-03-17 10:56:12,894][01299] Updated weights for policy 0, policy_version 765 (0.0010)
695
+ [2023-03-17 10:56:14,193][01299] Updated weights for policy 0, policy_version 775 (0.0011)
696
+ [2023-03-17 10:56:14,734][24380] Fps is (10 sec: 31948.8, 60 sec: 32153.6, 300 sec: 31647.1). Total num frames: 3190784. Throughput: 0: 8051.2. Samples: 739416. Policy #0 lag: (min: 0.0, avg: 1.3, max: 3.0)
697
+ [2023-03-17 10:56:14,735][24380] Avg episode reward: [(0, '22.320')]
698
+ [2023-03-17 10:56:15,457][01299] Updated weights for policy 0, policy_version 785 (0.0011)
699
+ [2023-03-17 10:56:16,726][01299] Updated weights for policy 0, policy_version 795 (0.0014)
700
+ [2023-03-17 10:56:17,985][01299] Updated weights for policy 0, policy_version 805 (0.0010)
701
+ [2023-03-17 10:56:19,278][01299] Updated weights for policy 0, policy_version 815 (0.0010)
702
+ [2023-03-17 10:56:19,734][24380] Fps is (10 sec: 31948.6, 60 sec: 32153.6, 300 sec: 31662.1). Total num frames: 3350528. Throughput: 0: 8047.0. Samples: 787348. Policy #0 lag: (min: 0.0, avg: 1.4, max: 3.0)
703
+ [2023-03-17 10:56:19,735][24380] Avg episode reward: [(0, '24.349')]
704
+ [2023-03-17 10:56:19,863][01277] Saving new best policy, reward=24.642!
705
+ [2023-03-17 10:56:20,556][01299] Updated weights for policy 0, policy_version 825 (0.0010)
706
+ [2023-03-17 10:56:21,799][01299] Updated weights for policy 0, policy_version 835 (0.0012)
707
+ [2023-03-17 10:56:23,077][01299] Updated weights for policy 0, policy_version 845 (0.0015)
708
+ [2023-03-17 10:56:24,359][01299] Updated weights for policy 0, policy_version 855 (0.0012)
709
+ [2023-03-17 10:56:24,734][24380] Fps is (10 sec: 32358.4, 60 sec: 32222.2, 300 sec: 31714.8). Total num frames: 3514368. Throughput: 0: 8045.7. Samples: 811526. Policy #0 lag: (min: 0.0, avg: 1.1, max: 3.0)
710
+ [2023-03-17 10:56:24,735][24380] Avg episode reward: [(0, '22.221')]
711
+ [2023-03-17 10:56:25,633][01299] Updated weights for policy 0, policy_version 865 (0.0010)
712
+ [2023-03-17 10:56:26,920][01299] Updated weights for policy 0, policy_version 875 (0.0011)
713
+ [2023-03-17 10:56:28,186][01299] Updated weights for policy 0, policy_version 885 (0.0012)
714
+ [2023-03-17 10:56:29,474][01299] Updated weights for policy 0, policy_version 895 (0.0010)
715
+ [2023-03-17 10:56:29,739][24380] Fps is (10 sec: 32342.2, 60 sec: 32219.2, 300 sec: 31724.0). Total num frames: 3674112. Throughput: 0: 8048.2. Samples: 859876. Policy #0 lag: (min: 0.0, avg: 1.0, max: 3.0)
716
+ [2023-03-17 10:56:29,741][24380] Avg episode reward: [(0, '24.978')]
717
+ [2023-03-17 10:56:29,746][01277] Saving /home/ckahmann/RL/train_dir/default_experiment/checkpoint_p0/checkpoint_000000897_3674112.pth...
718
+ [2023-03-17 10:56:29,863][01277] Saving new best policy, reward=26.247!
719
+ [2023-03-17 10:56:30,766][01299] Updated weights for policy 0, policy_version 905 (0.0009)
720
+ [2023-03-17 10:56:32,032][01299] Updated weights for policy 0, policy_version 915 (0.0010)
721
+ [2023-03-17 10:56:33,354][01299] Updated weights for policy 0, policy_version 925 (0.0010)
722
+ [2023-03-17 10:56:34,610][01299] Updated weights for policy 0, policy_version 935 (0.0012)
723
+ [2023-03-17 10:56:34,734][24380] Fps is (10 sec: 31948.6, 60 sec: 32221.9, 300 sec: 31735.1). Total num frames: 3833856. Throughput: 0: 8028.9. Samples: 907774. Policy #0 lag: (min: 0.0, avg: 1.2, max: 2.0)
724
+ [2023-03-17 10:56:34,735][24380] Avg episode reward: [(0, '24.749')]
725
+ [2023-03-17 10:56:35,882][01299] Updated weights for policy 0, policy_version 945 (0.0013)
726
+ [2023-03-17 10:56:37,173][01299] Updated weights for policy 0, policy_version 955 (0.0011)
727
+ [2023-03-17 10:56:38,451][01299] Updated weights for policy 0, policy_version 965 (0.0011)
728
+ [2023-03-17 10:56:39,707][01299] Updated weights for policy 0, policy_version 975 (0.0012)
729
+ [2023-03-17 10:56:39,734][24380] Fps is (10 sec: 31964.8, 60 sec: 32153.6, 300 sec: 31744.0). Total num frames: 3993600. Throughput: 0: 8021.1. Samples: 931680. Policy #0 lag: (min: 0.0, avg: 1.4, max: 3.0)
730
+ [2023-03-17 10:56:39,735][24380] Avg episode reward: [(0, '24.199')]
731
+ [2023-03-17 10:56:40,085][01277] Stopping Batcher_0...
732
+ [2023-03-17 10:56:40,086][01277] Loop batcher_evt_loop terminating...
733
+ [2023-03-17 10:56:40,087][01277] Saving /home/ckahmann/RL/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
734
+ [2023-03-17 10:56:40,085][24380] Component Batcher_0 stopped!
735
+ [2023-03-17 10:56:40,096][01334] Stopping RolloutWorker_w14...
736
+ [2023-03-17 10:56:40,097][01328] Stopping RolloutWorker_w10...
737
+ [2023-03-17 10:56:40,097][01334] Loop rollout_proc14_evt_loop terminating...
738
+ [2023-03-17 10:56:40,097][01328] Loop rollout_proc10_evt_loop terminating...
739
+ [2023-03-17 10:56:40,097][01319] Stopping RolloutWorker_w2...
740
+ [2023-03-17 10:56:40,097][24380] Component RolloutWorker_w14 stopped!
741
+ [2023-03-17 10:56:40,098][01301] Stopping RolloutWorker_w1...
742
+ [2023-03-17 10:56:40,098][01323] Stopping RolloutWorker_w5...
743
+ [2023-03-17 10:56:40,099][01326] Stopping RolloutWorker_w7...
744
+ [2023-03-17 10:56:40,098][01324] Stopping RolloutWorker_w6...
745
+ [2023-03-17 10:56:40,099][01319] Loop rollout_proc2_evt_loop terminating...
746
+ [2023-03-17 10:56:40,099][01301] Loop rollout_proc1_evt_loop terminating...
747
+ [2023-03-17 10:56:40,099][01323] Loop rollout_proc5_evt_loop terminating...
748
+ [2023-03-17 10:56:40,099][01332] Stopping RolloutWorker_w15...
749
+ [2023-03-17 10:56:40,099][01326] Loop rollout_proc7_evt_loop terminating...
750
+ [2023-03-17 10:56:40,099][01324] Loop rollout_proc6_evt_loop terminating...
751
+ [2023-03-17 10:56:40,099][01332] Loop rollout_proc15_evt_loop terminating...
752
+ [2023-03-17 10:56:40,099][01335] Stopping RolloutWorker_w13...
753
+ [2023-03-17 10:56:40,100][01335] Loop rollout_proc13_evt_loop terminating...
754
+ [2023-03-17 10:56:40,100][01325] Stopping RolloutWorker_w8...
755
+ [2023-03-17 10:56:40,099][24380] Component RolloutWorker_w10 stopped!
756
+ [2023-03-17 10:56:40,100][01321] Stopping RolloutWorker_w4...
757
+ [2023-03-17 10:56:40,101][01320] Stopping RolloutWorker_w3...
758
+ [2023-03-17 10:56:40,101][01325] Loop rollout_proc8_evt_loop terminating...
759
+ [2023-03-17 10:56:40,101][01320] Loop rollout_proc3_evt_loop terminating...
760
+ [2023-03-17 10:56:40,101][01321] Loop rollout_proc4_evt_loop terminating...
761
+ [2023-03-17 10:56:40,101][01329] Stopping RolloutWorker_w11...
762
+ [2023-03-17 10:56:40,102][24380] Component RolloutWorker_w2 stopped!
763
+ [2023-03-17 10:56:40,103][24380] Component RolloutWorker_w1 stopped!
764
+ [2023-03-17 10:56:40,104][24380] Component RolloutWorker_w6 stopped!
765
+ [2023-03-17 10:56:40,105][24380] Component RolloutWorker_w5 stopped!
766
+ [2023-03-17 10:56:40,106][24380] Component RolloutWorker_w7 stopped!
767
+ [2023-03-17 10:56:40,107][24380] Component RolloutWorker_w15 stopped!
768
+ [2023-03-17 10:56:40,108][24380] Component RolloutWorker_w13 stopped!
769
+ [2023-03-17 10:56:40,109][01331] Stopping RolloutWorker_w12...
770
+ [2023-03-17 10:56:40,109][24380] Component RolloutWorker_w8 stopped!
771
+ [2023-03-17 10:56:40,109][01331] Loop rollout_proc12_evt_loop terminating...
772
+ [2023-03-17 10:56:40,110][24380] Component RolloutWorker_w4 stopped!
773
+ [2023-03-17 10:56:40,110][24380] Component RolloutWorker_w3 stopped!
774
+ [2023-03-17 10:56:40,111][24380] Component RolloutWorker_w11 stopped!
775
+ [2023-03-17 10:56:40,112][24380] Component RolloutWorker_w12 stopped!
776
+ [2023-03-17 10:56:40,102][01329] Loop rollout_proc11_evt_loop terminating...
777
+ [2023-03-17 10:56:40,116][01327] Stopping RolloutWorker_w9...
778
+ [2023-03-17 10:56:40,116][01327] Loop rollout_proc9_evt_loop terminating...
779
+ [2023-03-17 10:56:40,116][24380] Component RolloutWorker_w9 stopped!
780
+ [2023-03-17 10:56:40,117][01300] Stopping RolloutWorker_w0...
781
+ [2023-03-17 10:56:40,118][01300] Loop rollout_proc0_evt_loop terminating...
782
+ [2023-03-17 10:56:40,117][24380] Component RolloutWorker_w0 stopped!
783
+ [2023-03-17 10:56:40,126][01299] Weights refcount: 2 0
784
+ [2023-03-17 10:56:40,127][01299] Stopping InferenceWorker_p0-w0...
785
+ [2023-03-17 10:56:40,128][01299] Loop inference_proc0-0_evt_loop terminating...
786
+ [2023-03-17 10:56:40,128][24380] Component InferenceWorker_p0-w0 stopped!
787
+ [2023-03-17 10:56:40,158][01277] Removing /home/ckahmann/RL/train_dir/default_experiment/checkpoint_p0/checkpoint_000000045_184320.pth
788
+ [2023-03-17 10:56:40,165][01277] Saving /home/ckahmann/RL/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
789
+ [2023-03-17 10:56:40,243][01277] Stopping LearnerWorker_p0...
790
+ [2023-03-17 10:56:40,243][01277] Loop learner_proc0_evt_loop terminating...
791
+ [2023-03-17 10:56:40,243][24380] Component LearnerWorker_p0 stopped!
792
+ [2023-03-17 10:56:40,245][24380] Waiting for process learner_proc0 to stop...
793
+ [2023-03-17 10:56:40,965][24380] Waiting for process inference_proc0-0 to join...
794
+ [2023-03-17 10:56:40,967][24380] Waiting for process rollout_proc0 to join...
795
+ [2023-03-17 10:56:40,968][24380] Waiting for process rollout_proc1 to join...
796
+ [2023-03-17 10:56:40,970][24380] Waiting for process rollout_proc2 to join...
797
+ [2023-03-17 10:56:40,971][24380] Waiting for process rollout_proc3 to join...
798
+ [2023-03-17 10:56:40,972][24380] Waiting for process rollout_proc4 to join...
799
+ [2023-03-17 10:56:40,974][24380] Waiting for process rollout_proc5 to join...
800
+ [2023-03-17 10:56:40,975][24380] Waiting for process rollout_proc6 to join...
801
+ [2023-03-17 10:56:40,976][24380] Waiting for process rollout_proc7 to join...
802
+ [2023-03-17 10:56:40,978][24380] Waiting for process rollout_proc8 to join...
803
+ [2023-03-17 10:56:40,979][24380] Waiting for process rollout_proc9 to join...
804
+ [2023-03-17 10:56:40,980][24380] Waiting for process rollout_proc10 to join...
805
+ [2023-03-17 10:56:40,982][24380] Waiting for process rollout_proc11 to join...
806
+ [2023-03-17 10:56:40,983][24380] Waiting for process rollout_proc12 to join...
807
+ [2023-03-17 10:56:40,984][24380] Waiting for process rollout_proc13 to join...
808
+ [2023-03-17 10:56:40,986][24380] Waiting for process rollout_proc14 to join...
809
+ [2023-03-17 10:56:40,987][24380] Waiting for process rollout_proc15 to join...
810
+ [2023-03-17 10:56:40,988][24380] Batcher 0 profile tree view:
811
+ batching: 12.2211, releasing_batches: 0.0246
812
+ [2023-03-17 10:56:40,989][24380] InferenceWorker_p0-w0 profile tree view:
813
+ wait_policy: 0.0001
814
+ wait_policy_total: 5.2569
815
+ update_model: 2.5986
816
+ weight_update: 0.0012
817
+ one_step: 0.0036
818
+ handle_policy_step: 107.7825
819
+ deserialize: 7.7872, stack: 0.7211, obs_to_device_normalize: 31.9304, forward: 37.9525, send_messages: 9.9044
820
+ prepare_outputs: 14.9006
821
+ to_cpu: 9.4684
822
+ [2023-03-17 10:56:40,990][24380] Learner 0 profile tree view:
823
+ misc: 0.0056, prepare_batch: 7.4842
824
+ train: 28.2527
825
+ epoch_init: 0.0042, minibatch_init: 0.0061, losses_postprocess: 0.1527, kl_divergence: 0.1730, after_optimizer: 0.3012
826
+ calculate_losses: 8.7205
827
+ losses_init: 0.0027, forward_head: 0.7555, bptt_initial: 5.6775, tail: 0.4340, advantages_returns: 0.1187, losses: 0.6629
828
+ bptt: 0.9171
829
+ bptt_forward_core: 0.8796
830
+ update: 18.5538
831
+ clip: 0.8257
832
+ [2023-03-17 10:56:40,992][24380] RolloutWorker_w0 profile tree view:
833
+ wait_for_trajectories: 0.0611, enqueue_policy_requests: 4.1572, env_step: 51.2831, overhead: 4.9311, complete_rollouts: 0.1200
834
+ save_policy_outputs: 4.2128
835
+ split_output_tensors: 2.0315
836
+ [2023-03-17 10:56:40,993][24380] RolloutWorker_w15 profile tree view:
837
+ wait_for_trajectories: 0.0640, enqueue_policy_requests: 4.2558, env_step: 53.0790, overhead: 5.1241, complete_rollouts: 0.1230
838
+ save_policy_outputs: 4.2865
839
+ split_output_tensors: 2.0872
840
+ [2023-03-17 10:56:40,995][24380] Loop Runner_EvtLoop terminating...
841
+ [2023-03-17 10:56:40,996][24380] Runner profile tree view:
842
+ main_loop: 129.7535
843
+ [2023-03-17 10:56:40,998][24380] Collected {0: 4005888}, FPS: 29452.5
844
+ [2023-03-17 10:58:47,379][24380] Loading existing experiment configuration from /home/ckahmann/RL/train_dir/default_experiment/config.json
845
+ [2023-03-17 10:58:47,380][24380] Overriding arg 'num_workers' with value 1 passed from command line
846
+ [2023-03-17 10:58:47,381][24380] Adding new argument 'no_render'=True that is not in the saved config file!
847
+ [2023-03-17 10:58:47,381][24380] Adding new argument 'save_video'=True that is not in the saved config file!
848
+ [2023-03-17 10:58:47,382][24380] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
849
+ [2023-03-17 10:58:47,382][24380] Adding new argument 'video_name'=None that is not in the saved config file!
850
+ [2023-03-17 10:58:47,383][24380] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file!
851
+ [2023-03-17 10:58:47,384][24380] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
852
+ [2023-03-17 10:58:47,384][24380] Adding new argument 'push_to_hub'=False that is not in the saved config file!
853
+ [2023-03-17 10:58:47,385][24380] Adding new argument 'hf_repository'=None that is not in the saved config file!
854
+ [2023-03-17 10:58:47,385][24380] Adding new argument 'policy_index'=0 that is not in the saved config file!
855
+ [2023-03-17 10:58:47,386][24380] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
856
+ [2023-03-17 10:58:47,387][24380] Adding new argument 'train_script'=None that is not in the saved config file!
857
+ [2023-03-17 10:58:47,387][24380] Adding new argument 'enjoy_script'=None that is not in the saved config file!
858
+ [2023-03-17 10:58:47,388][24380] Using frameskip 1 and render_action_repeat=4 for evaluation
859
+ [2023-03-17 10:58:47,404][24380] Doom resolution: 160x120, resize resolution: (128, 72)
860
+ [2023-03-17 10:58:47,406][24380] RunningMeanStd input shape: (3, 72, 128)
861
+ [2023-03-17 10:58:47,407][24380] RunningMeanStd input shape: (1,)
862
+ [2023-03-17 10:58:47,419][24380] ConvEncoder: input_channels=3
863
+ [2023-03-17 10:58:47,538][24380] Conv encoder output size: 512
864
+ [2023-03-17 10:58:47,539][24380] Policy head output size: 512
865
+ [2023-03-17 10:58:49,022][24380] Loading state from checkpoint /home/ckahmann/RL/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
866
+ [2023-03-17 10:58:49,781][24380] Num frames 100...
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+ [2023-03-17 10:58:49,931][24380] Num frames 200...
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+ [2023-03-17 10:58:50,058][24380] Num frames 300...
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+ [2023-03-17 10:58:50,290][24380] Num frames 500...
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+ [2023-03-17 10:58:50,397][24380] Num frames 600...
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+ [2023-03-17 10:58:50,508][24380] Num frames 700...
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+ [2023-03-17 10:58:50,619][24380] Num frames 800...
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+ [2023-03-17 10:58:50,731][24380] Num frames 900...
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+ [2023-03-17 10:58:50,956][24380] Num frames 1100...
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+ [2023-03-17 10:58:51,077][24380] Num frames 1200...
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+ [2023-03-17 10:58:51,193][24380] Avg episode rewards: #0: 27.480, true rewards: #0: 12.480
879
+ [2023-03-17 10:58:51,195][24380] Avg episode reward: 27.480, avg true_objective: 12.480
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+ [2023-03-17 10:58:51,292][24380] Num frames 1300...
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+ [2023-03-17 10:58:51,422][24380] Num frames 1400...
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+ [2023-03-17 10:58:51,579][24380] Num frames 1500...
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+ [2023-03-17 10:58:51,702][24380] Num frames 1600...
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+ [2023-03-17 10:58:51,863][24380] Avg episode rewards: #0: 17.405, true rewards: #0: 8.405
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+ [2023-03-17 10:58:51,865][24380] Avg episode reward: 17.405, avg true_objective: 8.405
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+ [2023-03-17 10:58:51,906][24380] Num frames 1700...
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+ [2023-03-17 10:58:52,054][24380] Num frames 1800...
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+ [2023-03-17 10:58:52,167][24380] Num frames 1900...
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+ [2023-03-17 10:58:52,273][24380] Avg episode rewards: #0: 12.457, true rewards: #0: 6.457
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+ [2023-03-17 10:58:52,275][24380] Avg episode reward: 12.457, avg true_objective: 6.457
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+ [2023-03-17 10:58:52,380][24380] Num frames 2000...
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+ [2023-03-17 10:58:53,369][24380] Num frames 2800...
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+ [2023-03-17 10:58:53,476][24380] Avg episode rewards: #0: 14.173, true rewards: #0: 7.172
901
+ [2023-03-17 10:58:53,478][24380] Avg episode reward: 14.173, avg true_objective: 7.172
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+ [2023-03-17 10:58:53,523][24380] Num frames 2900...
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+ [2023-03-17 10:58:54,674][24380] Num frames 4100...
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+ [2023-03-17 10:58:54,802][24380] Avg episode rewards: #0: 16.590, true rewards: #0: 8.390
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+ [2023-03-17 10:58:54,803][24380] Avg episode reward: 16.590, avg true_objective: 8.390
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+ [2023-03-17 10:58:54,816][24380] Num frames 4200...
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+ [2023-03-17 10:58:55,358][24380] Avg episode rewards: #0: 15.338, true rewards: #0: 8.005
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+ [2023-03-17 10:58:55,359][24380] Avg episode reward: 15.338, avg true_objective: 8.005
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+ [2023-03-17 10:58:55,457][24380] Num frames 4900...
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+ [2023-03-17 10:58:57,489][24380] Num frames 6900...
947
+ [2023-03-17 10:58:57,546][24380] Avg episode rewards: #0: 21.861, true rewards: #0: 9.861
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+ [2023-03-17 10:58:57,547][24380] Avg episode reward: 21.861, avg true_objective: 9.861
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+ [2023-03-17 10:58:57,644][24380] Num frames 7000...
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+ [2023-03-17 10:58:57,992][24380] Num frames 7300...
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+ [2023-03-17 10:58:58,072][24380] Avg episode rewards: #0: 19.899, true rewards: #0: 9.149
954
+ [2023-03-17 10:58:58,074][24380] Avg episode reward: 19.899, avg true_objective: 9.149
955
+ [2023-03-17 10:58:58,196][24380] Num frames 7400...
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+ [2023-03-17 10:58:58,399][24380] Num frames 7500...
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+ [2023-03-17 10:58:59,066][24380] Num frames 8000...
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+ [2023-03-17 10:59:00,031][24380] Num frames 8800...
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+ [2023-03-17 10:59:00,161][24380] Num frames 8900...
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+ [2023-03-17 10:59:00,596][24380] Num frames 9200...
974
+ [2023-03-17 10:59:00,665][24380] Avg episode rewards: #0: 22.674, true rewards: #0: 10.230
975
+ [2023-03-17 10:59:00,667][24380] Avg episode reward: 22.674, avg true_objective: 10.230
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+ [2023-03-17 10:59:00,847][24380] Num frames 9300...
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+ [2023-03-17 10:59:02,541][24380] Num frames 10500...
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+ [2023-03-17 10:59:02,701][24380] Avg episode rewards: #0: 23.768, true rewards: #0: 10.568
990
+ [2023-03-17 10:59:02,703][24380] Avg episode reward: 23.768, avg true_objective: 10.568
991
+ [2023-03-17 10:59:30,255][24380] Replay video saved to /home/ckahmann/RL/train_dir/default_experiment/replay.mp4!
992
+ [2023-03-17 11:02:12,760][24380] Loading existing experiment configuration from /home/ckahmann/RL/train_dir/default_experiment/config.json
993
+ [2023-03-17 11:02:12,761][24380] Overriding arg 'num_workers' with value 1 passed from command line
994
+ [2023-03-17 11:02:12,762][24380] Adding new argument 'no_render'=True that is not in the saved config file!
995
+ [2023-03-17 11:02:12,762][24380] Adding new argument 'save_video'=True that is not in the saved config file!
996
+ [2023-03-17 11:02:12,763][24380] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
997
+ [2023-03-17 11:02:12,764][24380] Adding new argument 'video_name'=None that is not in the saved config file!
998
+ [2023-03-17 11:02:12,764][24380] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!
999
+ [2023-03-17 11:02:12,765][24380] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
1000
+ [2023-03-17 11:02:12,766][24380] Adding new argument 'push_to_hub'=True that is not in the saved config file!
1001
+ [2023-03-17 11:02:12,766][24380] Adding new argument 'hf_repository'='Christian90/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file!
1002
+ [2023-03-17 11:02:12,767][24380] Adding new argument 'policy_index'=0 that is not in the saved config file!
1003
+ [2023-03-17 11:02:12,767][24380] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
1004
+ [2023-03-17 11:02:12,768][24380] Adding new argument 'train_script'=None that is not in the saved config file!
1005
+ [2023-03-17 11:02:12,769][24380] Adding new argument 'enjoy_script'=None that is not in the saved config file!
1006
+ [2023-03-17 11:02:12,769][24380] Using frameskip 1 and render_action_repeat=4 for evaluation
1007
+ [2023-03-17 11:02:12,788][24380] RunningMeanStd input shape: (3, 72, 128)
1008
+ [2023-03-17 11:02:12,790][24380] RunningMeanStd input shape: (1,)
1009
+ [2023-03-17 11:02:12,800][24380] ConvEncoder: input_channels=3
1010
+ [2023-03-17 11:02:12,828][24380] Conv encoder output size: 512
1011
+ [2023-03-17 11:02:12,829][24380] Policy head output size: 512
1012
+ [2023-03-17 11:02:12,863][24380] Loading state from checkpoint /home/ckahmann/RL/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
1013
+ [2023-03-17 11:02:13,294][24380] Num frames 100...
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+ [2023-03-17 11:02:14,133][24380] Num frames 800...
1021
+ [2023-03-17 11:02:14,262][24380] Avg episode rewards: #0: 20.640, true rewards: #0: 8.640
1022
+ [2023-03-17 11:02:14,263][24380] Avg episode reward: 20.640, avg true_objective: 8.640
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+ [2023-03-17 11:02:14,334][24380] Num frames 900...
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+ [2023-03-17 11:02:14,461][24380] Num frames 1000...
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+ [2023-03-17 11:02:16,599][24380] Num frames 2500...
1040
+ [2023-03-17 11:02:16,701][24380] Avg episode rewards: #0: 31.140, true rewards: #0: 12.640
1041
+ [2023-03-17 11:02:16,703][24380] Avg episode reward: 31.140, avg true_objective: 12.640
1042
+ [2023-03-17 11:02:16,836][24380] Num frames 2600...
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+ [2023-03-17 11:02:18,932][24380] Num frames 4200...
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+ [2023-03-17 11:02:19,128][24380] Avg episode rewards: #0: 36.960, true rewards: #0: 14.293
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+ [2023-03-17 11:02:19,130][24380] Avg episode reward: 36.960, avg true_objective: 14.293
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+ [2023-03-17 11:02:19,160][24380] Num frames 4300...
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+ [2023-03-17 11:02:19,884][24380] Avg episode rewards: #0: 29.750, true rewards: #0: 12.000
1068
+ [2023-03-17 11:02:19,886][24380] Avg episode reward: 29.750, avg true_objective: 12.000
1069
+ [2023-03-17 11:02:20,021][24380] Num frames 4900...
1070
+ [2023-03-17 11:02:20,140][24380] Num frames 5000...
1071
+ [2023-03-17 11:02:20,255][24380] Num frames 5100...
1072
+ [2023-03-17 11:02:20,373][24380] Num frames 5200...
1073
+ [2023-03-17 11:02:20,492][24380] Num frames 5300...
1074
+ [2023-03-17 11:02:20,635][24380] Num frames 5400...
1075
+ [2023-03-17 11:02:20,764][24380] Num frames 5500...
1076
+ [2023-03-17 11:02:20,892][24380] Num frames 5600...
1077
+ [2023-03-17 11:02:21,008][24380] Num frames 5700...
1078
+ [2023-03-17 11:02:21,126][24380] Avg episode rewards: #0: 28.088, true rewards: #0: 11.488
1079
+ [2023-03-17 11:02:21,128][24380] Avg episode reward: 28.088, avg true_objective: 11.488
1080
+ [2023-03-17 11:02:21,232][24380] Num frames 5800...
1081
+ [2023-03-17 11:02:21,376][24380] Num frames 5900...
1082
+ [2023-03-17 11:02:21,540][24380] Num frames 6000...
1083
+ [2023-03-17 11:02:21,661][24380] Num frames 6100...
1084
+ [2023-03-17 11:02:21,809][24380] Num frames 6200...
1085
+ [2023-03-17 11:02:21,940][24380] Num frames 6300...
1086
+ [2023-03-17 11:02:22,059][24380] Num frames 6400...
1087
+ [2023-03-17 11:02:22,172][24380] Num frames 6500...
1088
+ [2023-03-17 11:02:22,285][24380] Num frames 6600...
1089
+ [2023-03-17 11:02:22,414][24380] Num frames 6700...
1090
+ [2023-03-17 11:02:22,534][24380] Num frames 6800...
1091
+ [2023-03-17 11:02:22,647][24380] Num frames 6900...
1092
+ [2023-03-17 11:02:22,762][24380] Num frames 7000...
1093
+ [2023-03-17 11:02:22,885][24380] Num frames 7100...
1094
+ [2023-03-17 11:02:23,036][24380] Num frames 7200...
1095
+ [2023-03-17 11:02:23,152][24380] Num frames 7300...
1096
+ [2023-03-17 11:02:23,292][24380] Num frames 7400...
1097
+ [2023-03-17 11:02:23,418][24380] Num frames 7500...
1098
+ [2023-03-17 11:02:23,504][24380] Num frames 7600...
1099
+ [2023-03-17 11:02:23,589][24380] Num frames 7700...
1100
+ [2023-03-17 11:02:23,717][24380] Num frames 7800...
1101
+ [2023-03-17 11:02:23,830][24380] Avg episode rewards: #0: 32.406, true rewards: #0: 13.073
1102
+ [2023-03-17 11:02:23,832][24380] Avg episode reward: 32.406, avg true_objective: 13.073
1103
+ [2023-03-17 11:02:23,947][24380] Num frames 7900...
1104
+ [2023-03-17 11:02:24,092][24380] Num frames 8000...
1105
+ [2023-03-17 11:02:24,247][24380] Num frames 8100...
1106
+ [2023-03-17 11:02:24,367][24380] Num frames 8200...
1107
+ [2023-03-17 11:02:24,487][24380] Num frames 8300...
1108
+ [2023-03-17 11:02:24,609][24380] Num frames 8400...
1109
+ [2023-03-17 11:02:24,724][24380] Num frames 8500...
1110
+ [2023-03-17 11:02:24,835][24380] Num frames 8600...
1111
+ [2023-03-17 11:02:24,998][24380] Avg episode rewards: #0: 29.965, true rewards: #0: 12.394
1112
+ [2023-03-17 11:02:25,000][24380] Avg episode reward: 29.965, avg true_objective: 12.394
1113
+ [2023-03-17 11:02:25,050][24380] Num frames 8700...
1114
+ [2023-03-17 11:02:25,182][24380] Num frames 8800...
1115
+ [2023-03-17 11:02:25,289][24380] Num frames 8900...
1116
+ [2023-03-17 11:02:25,402][24380] Num frames 9000...
1117
+ [2023-03-17 11:02:25,569][24380] Num frames 9100...
1118
+ [2023-03-17 11:02:25,681][24380] Num frames 9200...
1119
+ [2023-03-17 11:02:25,829][24380] Num frames 9300...
1120
+ [2023-03-17 11:02:26,014][24380] Num frames 9400...
1121
+ [2023-03-17 11:02:26,142][24380] Num frames 9500...
1122
+ [2023-03-17 11:02:26,264][24380] Num frames 9600...
1123
+ [2023-03-17 11:02:26,370][24380] Avg episode rewards: #0: 28.929, true rewards: #0: 12.054
1124
+ [2023-03-17 11:02:26,372][24380] Avg episode reward: 28.929, avg true_objective: 12.054
1125
+ [2023-03-17 11:02:26,492][24380] Num frames 9700...
1126
+ [2023-03-17 11:02:26,650][24380] Num frames 9800...
1127
+ [2023-03-17 11:02:26,798][24380] Num frames 9900...
1128
+ [2023-03-17 11:02:26,914][24380] Num frames 10000...
1129
+ [2023-03-17 11:02:27,105][24380] Num frames 10100...
1130
+ [2023-03-17 11:02:27,295][24380] Num frames 10200...
1131
+ [2023-03-17 11:02:27,483][24380] Num frames 10300...
1132
+ [2023-03-17 11:02:27,662][24380] Num frames 10400...
1133
+ [2023-03-17 11:02:27,784][24380] Num frames 10500...
1134
+ [2023-03-17 11:02:27,902][24380] Num frames 10600...
1135
+ [2023-03-17 11:02:27,963][24380] Avg episode rewards: #0: 28.670, true rewards: #0: 11.781
1136
+ [2023-03-17 11:02:27,965][24380] Avg episode reward: 28.670, avg true_objective: 11.781
1137
+ [2023-03-17 11:02:28,112][24380] Num frames 10700...
1138
+ [2023-03-17 11:02:28,238][24380] Num frames 10800...
1139
+ [2023-03-17 11:02:28,364][24380] Num frames 10900...
1140
+ [2023-03-17 11:02:28,495][24380] Num frames 11000...
1141
+ [2023-03-17 11:02:28,621][24380] Num frames 11100...
1142
+ [2023-03-17 11:02:28,756][24380] Num frames 11200...
1143
+ [2023-03-17 11:02:28,887][24380] Num frames 11300...
1144
+ [2023-03-17 11:02:29,022][24380] Num frames 11400...
1145
+ [2023-03-17 11:02:29,149][24380] Num frames 11500...
1146
+ [2023-03-17 11:02:29,269][24380] Num frames 11600...
1147
+ [2023-03-17 11:02:29,429][24380] Avg episode rewards: #0: 28.480, true rewards: #0: 11.680
1148
+ [2023-03-17 11:02:29,431][24380] Avg episode reward: 28.480, avg true_objective: 11.680
1149
+ [2023-03-17 11:02:59,990][24380] Replay video saved to /home/ckahmann/RL/train_dir/default_experiment/replay.mp4!