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markeidsaune
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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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  7. config.json +142 -0
  8. replay.mp4 +3 -0
  9. sf_log.txt +706 -0
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+ ---
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+ library_name: sample-factory
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+ tags:
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - sample-factory
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+ model-index:
8
+ - name: APPO
9
+ results:
10
+ - task:
11
+ type: reinforcement-learning
12
+ name: reinforcement-learning
13
+ dataset:
14
+ name: doom_health_gathering_supreme
15
+ type: doom_health_gathering_supreme
16
+ metrics:
17
+ - type: mean_reward
18
+ value: 9.31 +/- 4.84
19
+ name: mean_reward
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+ verified: false
21
+ ---
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+
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+ A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
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+
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+ This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
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+ 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 markeidsaune/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/mark/rl_course/unit8/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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+ "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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+ "normalize_input": true,
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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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+ "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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+ "rnn_size": 512,
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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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+ "wandb_tags": [],
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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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+ "res_h": 72,
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+ "wide_aspect_ratio": false,
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+ "eval_env_frameskip": 1,
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+ "fps": 35,
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+ "command_line": "--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000",
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+ "cli_args": {
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+ "env": "doom_health_gathering_supreme",
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+ "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
+ [2023-05-24 20:15:24,322][2722668] Saving configuration to /home/mark/rl_course/unit8/train_dir/default_experiment/config.json...
2
+ [2023-05-24 20:15:24,325][2722668] Rollout worker 0 uses device cpu
3
+ [2023-05-24 20:15:24,326][2722668] Rollout worker 1 uses device cpu
4
+ [2023-05-24 20:15:24,327][2722668] Rollout worker 2 uses device cpu
5
+ [2023-05-24 20:15:24,328][2722668] Rollout worker 3 uses device cpu
6
+ [2023-05-24 20:15:24,329][2722668] Rollout worker 4 uses device cpu
7
+ [2023-05-24 20:15:24,331][2722668] Rollout worker 5 uses device cpu
8
+ [2023-05-24 20:15:24,332][2722668] Rollout worker 6 uses device cpu
9
+ [2023-05-24 20:15:24,333][2722668] Rollout worker 7 uses device cpu
10
+ [2023-05-24 20:15:24,397][2722668] Using GPUs [0] for process 0 (actually maps to GPUs [0])
11
+ [2023-05-24 20:15:24,398][2722668] InferenceWorker_p0-w0: min num requests: 2
12
+ [2023-05-24 20:15:24,427][2722668] Starting all processes...
13
+ [2023-05-24 20:15:24,428][2722668] Starting process learner_proc0
14
+ [2023-05-24 20:15:24,476][2722668] Starting all processes...
15
+ [2023-05-24 20:15:24,485][2722668] Starting process inference_proc0-0
16
+ [2023-05-24 20:15:24,485][2722668] Starting process rollout_proc0
17
+ [2023-05-24 20:15:24,485][2722668] Starting process rollout_proc1
18
+ [2023-05-24 20:15:24,486][2722668] Starting process rollout_proc2
19
+ [2023-05-24 20:15:24,486][2722668] Starting process rollout_proc3
20
+ [2023-05-24 20:15:24,487][2722668] Starting process rollout_proc4
21
+ [2023-05-24 20:15:24,487][2722668] Starting process rollout_proc5
22
+ [2023-05-24 20:15:24,488][2722668] Starting process rollout_proc6
23
+ [2023-05-24 20:15:24,488][2722668] Starting process rollout_proc7
24
+ [2023-05-24 20:15:26,022][2737021] Using GPUs [0] for process 0 (actually maps to GPUs [0])
25
+ [2023-05-24 20:15:26,022][2737021] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
26
+ [2023-05-24 20:15:26,039][2737021] Num visible devices: 1
27
+ [2023-05-24 20:15:26,061][2737021] Starting seed is not provided
28
+ [2023-05-24 20:15:26,062][2737021] Using GPUs [0] for process 0 (actually maps to GPUs [0])
29
+ [2023-05-24 20:15:26,062][2737021] Initializing actor-critic model on device cuda:0
30
+ [2023-05-24 20:15:26,062][2737021] RunningMeanStd input shape: (3, 72, 128)
31
+ [2023-05-24 20:15:26,063][2737021] RunningMeanStd input shape: (1,)
32
+ [2023-05-24 20:15:26,077][2737021] ConvEncoder: input_channels=3
33
+ [2023-05-24 20:15:26,147][2737054] Worker 7 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
34
+ [2023-05-24 20:15:26,181][2737046] Using GPUs [0] for process 0 (actually maps to GPUs [0])
35
+ [2023-05-24 20:15:26,181][2737046] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
36
+ [2023-05-24 20:15:26,189][2737053] Worker 6 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
37
+ [2023-05-24 20:15:26,191][2737049] Worker 0 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
38
+ [2023-05-24 20:15:26,193][2737047] Worker 2 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
39
+ [2023-05-24 20:15:26,200][2737046] Num visible devices: 1
40
+ [2023-05-24 20:15:26,200][2737052] Worker 4 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
41
+ [2023-05-24 20:15:26,201][2737048] Worker 1 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
42
+ [2023-05-24 20:15:26,203][2737051] Worker 5 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
43
+ [2023-05-24 20:15:26,203][2737021] Conv encoder output size: 512
44
+ [2023-05-24 20:15:26,204][2737021] Policy head output size: 512
45
+ [2023-05-24 20:15:26,217][2737021] Created Actor Critic model with architecture:
46
+ [2023-05-24 20:15:26,217][2737050] Worker 3 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
47
+ [2023-05-24 20:15:26,217][2737021] ActorCriticSharedWeights(
48
+ (obs_normalizer): ObservationNormalizer(
49
+ (running_mean_std): RunningMeanStdDictInPlace(
50
+ (running_mean_std): ModuleDict(
51
+ (obs): RunningMeanStdInPlace()
52
+ )
53
+ )
54
+ )
55
+ (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
56
+ (encoder): VizdoomEncoder(
57
+ (basic_encoder): ConvEncoder(
58
+ (enc): RecursiveScriptModule(
59
+ original_name=ConvEncoderImpl
60
+ (conv_head): RecursiveScriptModule(
61
+ original_name=Sequential
62
+ (0): RecursiveScriptModule(original_name=Conv2d)
63
+ (1): RecursiveScriptModule(original_name=ELU)
64
+ (2): RecursiveScriptModule(original_name=Conv2d)
65
+ (3): RecursiveScriptModule(original_name=ELU)
66
+ (4): RecursiveScriptModule(original_name=Conv2d)
67
+ (5): RecursiveScriptModule(original_name=ELU)
68
+ )
69
+ (mlp_layers): RecursiveScriptModule(
70
+ original_name=Sequential
71
+ (0): RecursiveScriptModule(original_name=Linear)
72
+ (1): RecursiveScriptModule(original_name=ELU)
73
+ )
74
+ )
75
+ )
76
+ )
77
+ (core): ModelCoreRNN(
78
+ (core): GRU(512, 512)
79
+ )
80
+ (decoder): MlpDecoder(
81
+ (mlp): Identity()
82
+ )
83
+ (critic_linear): Linear(in_features=512, out_features=1, bias=True)
84
+ (action_parameterization): ActionParameterizationDefault(
85
+ (distribution_linear): Linear(in_features=512, out_features=5, bias=True)
86
+ )
87
+ )
88
+ [2023-05-24 20:15:28,728][2737021] Using optimizer <class 'torch.optim.adam.Adam'>
89
+ [2023-05-24 20:15:28,729][2737021] No checkpoints found
90
+ [2023-05-24 20:15:28,729][2737021] Did not load from checkpoint, starting from scratch!
91
+ [2023-05-24 20:15:28,729][2737021] Initialized policy 0 weights for model version 0
92
+ [2023-05-24 20:15:28,731][2737021] LearnerWorker_p0 finished initialization!
93
+ [2023-05-24 20:15:28,731][2737021] Using GPUs [0] for process 0 (actually maps to GPUs [0])
94
+ [2023-05-24 20:15:28,856][2737046] RunningMeanStd input shape: (3, 72, 128)
95
+ [2023-05-24 20:15:28,857][2737046] RunningMeanStd input shape: (1,)
96
+ [2023-05-24 20:15:28,872][2737046] ConvEncoder: input_channels=3
97
+ [2023-05-24 20:15:29,004][2737046] Conv encoder output size: 512
98
+ [2023-05-24 20:15:29,004][2737046] Policy head output size: 512
99
+ [2023-05-24 20:15:30,976][2722668] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 0. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
100
+ [2023-05-24 20:15:31,437][2722668] Inference worker 0-0 is ready!
101
+ [2023-05-24 20:15:31,438][2722668] All inference workers are ready! Signal rollout workers to start!
102
+ [2023-05-24 20:15:31,473][2737047] Doom resolution: 160x120, resize resolution: (128, 72)
103
+ [2023-05-24 20:15:31,496][2737051] Doom resolution: 160x120, resize resolution: (128, 72)
104
+ [2023-05-24 20:15:31,499][2737050] Doom resolution: 160x120, resize resolution: (128, 72)
105
+ [2023-05-24 20:15:31,502][2737053] Doom resolution: 160x120, resize resolution: (128, 72)
106
+ [2023-05-24 20:15:31,502][2737054] Doom resolution: 160x120, resize resolution: (128, 72)
107
+ [2023-05-24 20:15:31,502][2737049] Doom resolution: 160x120, resize resolution: (128, 72)
108
+ [2023-05-24 20:15:31,508][2737052] Doom resolution: 160x120, resize resolution: (128, 72)
109
+ [2023-05-24 20:15:31,510][2737048] Doom resolution: 160x120, resize resolution: (128, 72)
110
+ [2023-05-24 20:15:32,115][2737047] Decorrelating experience for 0 frames...
111
+ [2023-05-24 20:15:32,121][2737049] Decorrelating experience for 0 frames...
112
+ [2023-05-24 20:15:32,121][2737052] Decorrelating experience for 0 frames...
113
+ [2023-05-24 20:15:32,122][2737048] Decorrelating experience for 0 frames...
114
+ [2023-05-24 20:15:32,123][2737054] Decorrelating experience for 0 frames...
115
+ [2023-05-24 20:15:32,123][2737050] Decorrelating experience for 0 frames...
116
+ [2023-05-24 20:15:32,430][2737052] Decorrelating experience for 32 frames...
117
+ [2023-05-24 20:15:32,433][2737050] Decorrelating experience for 32 frames...
118
+ [2023-05-24 20:15:32,438][2737049] Decorrelating experience for 32 frames...
119
+ [2023-05-24 20:15:32,439][2737054] Decorrelating experience for 32 frames...
120
+ [2023-05-24 20:15:32,482][2737047] Decorrelating experience for 32 frames...
121
+ [2023-05-24 20:15:32,772][2737051] Decorrelating experience for 0 frames...
122
+ [2023-05-24 20:15:32,789][2737052] Decorrelating experience for 64 frames...
123
+ [2023-05-24 20:15:32,792][2737053] Decorrelating experience for 0 frames...
124
+ [2023-05-24 20:15:32,801][2737050] Decorrelating experience for 64 frames...
125
+ [2023-05-24 20:15:32,808][2737048] Decorrelating experience for 32 frames...
126
+ [2023-05-24 20:15:33,085][2737051] Decorrelating experience for 32 frames...
127
+ [2023-05-24 20:15:33,105][2737053] Decorrelating experience for 32 frames...
128
+ [2023-05-24 20:15:33,148][2737047] Decorrelating experience for 64 frames...
129
+ [2023-05-24 20:15:33,151][2737049] Decorrelating experience for 64 frames...
130
+ [2023-05-24 20:15:33,163][2737054] Decorrelating experience for 64 frames...
131
+ [2023-05-24 20:15:33,173][2737048] Decorrelating experience for 64 frames...
132
+ [2023-05-24 20:15:33,445][2737051] Decorrelating experience for 64 frames...
133
+ [2023-05-24 20:15:33,456][2737053] Decorrelating experience for 64 frames...
134
+ [2023-05-24 20:15:33,471][2737050] Decorrelating experience for 96 frames...
135
+ [2023-05-24 20:15:33,511][2737047] Decorrelating experience for 96 frames...
136
+ [2023-05-24 20:15:33,788][2737054] Decorrelating experience for 96 frames...
137
+ [2023-05-24 20:15:33,826][2737053] Decorrelating experience for 96 frames...
138
+ [2023-05-24 20:15:33,836][2737051] Decorrelating experience for 96 frames...
139
+ [2023-05-24 20:15:34,104][2737048] Decorrelating experience for 96 frames...
140
+ [2023-05-24 20:15:34,418][2737052] Decorrelating experience for 96 frames...
141
+ [2023-05-24 20:15:34,783][2737049] Decorrelating experience for 96 frames...
142
+ [2023-05-24 20:15:34,984][2737021] Signal inference workers to stop experience collection...
143
+ [2023-05-24 20:15:34,992][2737046] InferenceWorker_p0-w0: stopping experience collection
144
+ [2023-05-24 20:15:35,976][2722668] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 62.8. Samples: 314. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
145
+ [2023-05-24 20:15:35,978][2722668] Avg episode reward: [(0, '2.719')]
146
+ [2023-05-24 20:15:36,386][2737021] Signal inference workers to resume experience collection...
147
+ [2023-05-24 20:15:36,387][2737046] InferenceWorker_p0-w0: resuming experience collection
148
+ [2023-05-24 20:15:38,875][2737046] Updated weights for policy 0, policy_version 10 (0.0469)
149
+ [2023-05-24 20:15:40,695][2737046] Updated weights for policy 0, policy_version 20 (0.0009)
150
+ [2023-05-24 20:15:40,976][2722668] Fps is (10 sec: 8601.6, 60 sec: 8601.6, 300 sec: 8601.6). Total num frames: 86016. Throughput: 0: 1969.0. Samples: 19690. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
151
+ [2023-05-24 20:15:40,977][2722668] Avg episode reward: [(0, '4.404')]
152
+ [2023-05-24 20:15:42,521][2737046] Updated weights for policy 0, policy_version 30 (0.0008)
153
+ [2023-05-24 20:15:44,344][2737046] Updated weights for policy 0, policy_version 40 (0.0009)
154
+ [2023-05-24 20:15:44,390][2722668] Heartbeat connected on Batcher_0
155
+ [2023-05-24 20:15:44,393][2722668] Heartbeat connected on LearnerWorker_p0
156
+ [2023-05-24 20:15:44,399][2722668] Heartbeat connected on InferenceWorker_p0-w0
157
+ [2023-05-24 20:15:44,404][2722668] Heartbeat connected on RolloutWorker_w0
158
+ [2023-05-24 20:15:44,405][2722668] Heartbeat connected on RolloutWorker_w1
159
+ [2023-05-24 20:15:44,409][2722668] Heartbeat connected on RolloutWorker_w2
160
+ [2023-05-24 20:15:44,415][2722668] Heartbeat connected on RolloutWorker_w3
161
+ [2023-05-24 20:15:44,422][2722668] Heartbeat connected on RolloutWorker_w4
162
+ [2023-05-24 20:15:44,423][2722668] Heartbeat connected on RolloutWorker_w5
163
+ [2023-05-24 20:15:44,425][2722668] Heartbeat connected on RolloutWorker_w6
164
+ [2023-05-24 20:15:44,427][2722668] Heartbeat connected on RolloutWorker_w7
165
+ [2023-05-24 20:15:45,976][2722668] Fps is (10 sec: 19661.0, 60 sec: 13107.2, 300 sec: 13107.2). Total num frames: 196608. Throughput: 0: 2436.5. Samples: 36548. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
166
+ [2023-05-24 20:15:45,977][2722668] Avg episode reward: [(0, '4.379')]
167
+ [2023-05-24 20:15:45,999][2737021] Saving new best policy, reward=4.379!
168
+ [2023-05-24 20:15:46,177][2737046] Updated weights for policy 0, policy_version 50 (0.0008)
169
+ [2023-05-24 20:15:48,003][2737046] Updated weights for policy 0, policy_version 60 (0.0009)
170
+ [2023-05-24 20:15:49,817][2737046] Updated weights for policy 0, policy_version 70 (0.0008)
171
+ [2023-05-24 20:15:50,976][2722668] Fps is (10 sec: 22528.0, 60 sec: 15564.8, 300 sec: 15564.8). Total num frames: 311296. Throughput: 0: 3517.5. Samples: 70350. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
172
+ [2023-05-24 20:15:50,977][2722668] Avg episode reward: [(0, '4.638')]
173
+ [2023-05-24 20:15:50,982][2737021] Saving new best policy, reward=4.638!
174
+ [2023-05-24 20:15:51,666][2737046] Updated weights for policy 0, policy_version 80 (0.0008)
175
+ [2023-05-24 20:15:53,506][2737046] Updated weights for policy 0, policy_version 90 (0.0009)
176
+ [2023-05-24 20:15:55,344][2737046] Updated weights for policy 0, policy_version 100 (0.0009)
177
+ [2023-05-24 20:15:55,976][2722668] Fps is (10 sec: 22528.1, 60 sec: 16875.5, 300 sec: 16875.5). Total num frames: 421888. Throughput: 0: 4150.5. Samples: 103762. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
178
+ [2023-05-24 20:15:55,977][2722668] Avg episode reward: [(0, '4.824')]
179
+ [2023-05-24 20:15:55,978][2737021] Saving new best policy, reward=4.824!
180
+ [2023-05-24 20:15:57,161][2737046] Updated weights for policy 0, policy_version 110 (0.0009)
181
+ [2023-05-24 20:15:58,982][2737046] Updated weights for policy 0, policy_version 120 (0.0008)
182
+ [2023-05-24 20:16:00,811][2737046] Updated weights for policy 0, policy_version 130 (0.0008)
183
+ [2023-05-24 20:16:00,976][2722668] Fps is (10 sec: 22118.3, 60 sec: 17749.3, 300 sec: 17749.3). Total num frames: 532480. Throughput: 0: 4018.2. Samples: 120546. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
184
+ [2023-05-24 20:16:00,977][2722668] Avg episode reward: [(0, '4.588')]
185
+ [2023-05-24 20:16:02,629][2737046] Updated weights for policy 0, policy_version 140 (0.0010)
186
+ [2023-05-24 20:16:04,461][2737046] Updated weights for policy 0, policy_version 150 (0.0009)
187
+ [2023-05-24 20:16:05,976][2722668] Fps is (10 sec: 22527.9, 60 sec: 18490.5, 300 sec: 18490.5). Total num frames: 647168. Throughput: 0: 4408.3. Samples: 154290. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
188
+ [2023-05-24 20:16:05,977][2722668] Avg episode reward: [(0, '4.587')]
189
+ [2023-05-24 20:16:06,238][2737046] Updated weights for policy 0, policy_version 160 (0.0008)
190
+ [2023-05-24 20:16:08,048][2737046] Updated weights for policy 0, policy_version 170 (0.0008)
191
+ [2023-05-24 20:16:09,867][2737046] Updated weights for policy 0, policy_version 180 (0.0009)
192
+ [2023-05-24 20:16:10,976][2722668] Fps is (10 sec: 22937.7, 60 sec: 19046.4, 300 sec: 19046.4). Total num frames: 761856. Throughput: 0: 4703.9. Samples: 188156. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
193
+ [2023-05-24 20:16:10,977][2722668] Avg episode reward: [(0, '4.869')]
194
+ [2023-05-24 20:16:10,985][2737021] Saving new best policy, reward=4.869!
195
+ [2023-05-24 20:16:11,691][2737046] Updated weights for policy 0, policy_version 190 (0.0009)
196
+ [2023-05-24 20:16:13,543][2737046] Updated weights for policy 0, policy_version 200 (0.0008)
197
+ [2023-05-24 20:16:15,376][2737046] Updated weights for policy 0, policy_version 210 (0.0009)
198
+ [2023-05-24 20:16:15,976][2722668] Fps is (10 sec: 22528.1, 60 sec: 19387.7, 300 sec: 19387.7). Total num frames: 872448. Throughput: 0: 4553.1. Samples: 204890. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
199
+ [2023-05-24 20:16:15,977][2722668] Avg episode reward: [(0, '5.324')]
200
+ [2023-05-24 20:16:15,978][2737021] Saving new best policy, reward=5.324!
201
+ [2023-05-24 20:16:17,217][2737046] Updated weights for policy 0, policy_version 220 (0.0008)
202
+ [2023-05-24 20:16:19,046][2737046] Updated weights for policy 0, policy_version 230 (0.0010)
203
+ [2023-05-24 20:16:20,891][2737046] Updated weights for policy 0, policy_version 240 (0.0008)
204
+ [2023-05-24 20:16:20,976][2722668] Fps is (10 sec: 22118.4, 60 sec: 19660.8, 300 sec: 19660.8). Total num frames: 983040. Throughput: 0: 5292.6. Samples: 238478. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
205
+ [2023-05-24 20:16:20,977][2722668] Avg episode reward: [(0, '5.011')]
206
+ [2023-05-24 20:16:22,750][2737046] Updated weights for policy 0, policy_version 250 (0.0009)
207
+ [2023-05-24 20:16:24,611][2737046] Updated weights for policy 0, policy_version 260 (0.0009)
208
+ [2023-05-24 20:16:25,976][2722668] Fps is (10 sec: 22118.2, 60 sec: 19884.2, 300 sec: 19884.2). Total num frames: 1093632. Throughput: 0: 5603.0. Samples: 271824. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
209
+ [2023-05-24 20:16:25,977][2722668] Avg episode reward: [(0, '5.167')]
210
+ [2023-05-24 20:16:26,448][2737046] Updated weights for policy 0, policy_version 270 (0.0009)
211
+ [2023-05-24 20:16:28,301][2737046] Updated weights for policy 0, policy_version 280 (0.0009)
212
+ [2023-05-24 20:16:30,111][2737046] Updated weights for policy 0, policy_version 290 (0.0009)
213
+ [2023-05-24 20:16:30,976][2722668] Fps is (10 sec: 22118.4, 60 sec: 20070.4, 300 sec: 20070.4). Total num frames: 1204224. Throughput: 0: 5598.6. Samples: 288486. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
214
+ [2023-05-24 20:16:30,977][2722668] Avg episode reward: [(0, '5.892')]
215
+ [2023-05-24 20:16:31,012][2737021] Saving new best policy, reward=5.892!
216
+ [2023-05-24 20:16:31,920][2737046] Updated weights for policy 0, policy_version 300 (0.0008)
217
+ [2023-05-24 20:16:33,732][2737046] Updated weights for policy 0, policy_version 310 (0.0008)
218
+ [2023-05-24 20:16:35,541][2737046] Updated weights for policy 0, policy_version 320 (0.0009)
219
+ [2023-05-24 20:16:35,976][2722668] Fps is (10 sec: 22528.3, 60 sec: 21981.9, 300 sec: 20291.0). Total num frames: 1318912. Throughput: 0: 5597.6. Samples: 322244. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
220
+ [2023-05-24 20:16:35,977][2722668] Avg episode reward: [(0, '6.006')]
221
+ [2023-05-24 20:16:35,978][2737021] Saving new best policy, reward=6.006!
222
+ [2023-05-24 20:16:37,335][2737046] Updated weights for policy 0, policy_version 330 (0.0008)
223
+ [2023-05-24 20:16:39,153][2737046] Updated weights for policy 0, policy_version 340 (0.0008)
224
+ [2023-05-24 20:16:40,976][2722668] Fps is (10 sec: 22528.0, 60 sec: 22391.5, 300 sec: 20421.5). Total num frames: 1429504. Throughput: 0: 5605.9. Samples: 356028. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
225
+ [2023-05-24 20:16:40,977][2722668] Avg episode reward: [(0, '8.106')]
226
+ [2023-05-24 20:16:40,983][2737021] Saving new best policy, reward=8.106!
227
+ [2023-05-24 20:16:40,984][2737046] Updated weights for policy 0, policy_version 350 (0.0009)
228
+ [2023-05-24 20:16:42,811][2737046] Updated weights for policy 0, policy_version 360 (0.0008)
229
+ [2023-05-24 20:16:44,631][2737046] Updated weights for policy 0, policy_version 370 (0.0008)
230
+ [2023-05-24 20:16:45,976][2722668] Fps is (10 sec: 22527.8, 60 sec: 22459.7, 300 sec: 20589.2). Total num frames: 1544192. Throughput: 0: 5609.3. Samples: 372964. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
231
+ [2023-05-24 20:16:45,977][2722668] Avg episode reward: [(0, '7.390')]
232
+ [2023-05-24 20:16:46,458][2737046] Updated weights for policy 0, policy_version 380 (0.0008)
233
+ [2023-05-24 20:16:48,289][2737046] Updated weights for policy 0, policy_version 390 (0.0008)
234
+ [2023-05-24 20:16:50,129][2737046] Updated weights for policy 0, policy_version 400 (0.0009)
235
+ [2023-05-24 20:16:50,976][2722668] Fps is (10 sec: 22528.0, 60 sec: 22391.5, 300 sec: 20684.8). Total num frames: 1654784. Throughput: 0: 5607.5. Samples: 406626. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
236
+ [2023-05-24 20:16:50,977][2722668] Avg episode reward: [(0, '7.569')]
237
+ [2023-05-24 20:16:51,933][2737046] Updated weights for policy 0, policy_version 410 (0.0009)
238
+ [2023-05-24 20:16:53,746][2737046] Updated weights for policy 0, policy_version 420 (0.0009)
239
+ [2023-05-24 20:16:55,581][2737046] Updated weights for policy 0, policy_version 430 (0.0008)
240
+ [2023-05-24 20:16:55,976][2722668] Fps is (10 sec: 22528.1, 60 sec: 22459.7, 300 sec: 20817.3). Total num frames: 1769472. Throughput: 0: 5605.7. Samples: 440412. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
241
+ [2023-05-24 20:16:55,977][2722668] Avg episode reward: [(0, '8.576')]
242
+ [2023-05-24 20:16:55,978][2737021] Saving new best policy, reward=8.576!
243
+ [2023-05-24 20:16:57,388][2737046] Updated weights for policy 0, policy_version 440 (0.0009)
244
+ [2023-05-24 20:16:59,202][2737046] Updated weights for policy 0, policy_version 450 (0.0008)
245
+ [2023-05-24 20:17:00,974][2737046] Updated weights for policy 0, policy_version 460 (0.0009)
246
+ [2023-05-24 20:17:00,976][2722668] Fps is (10 sec: 22937.6, 60 sec: 22528.0, 300 sec: 20935.1). Total num frames: 1884160. Throughput: 0: 5609.6. Samples: 457320. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
247
+ [2023-05-24 20:17:00,977][2722668] Avg episode reward: [(0, '11.579')]
248
+ [2023-05-24 20:17:00,982][2737021] Saving new best policy, reward=11.579!
249
+ [2023-05-24 20:17:02,777][2737046] Updated weights for policy 0, policy_version 470 (0.0010)
250
+ [2023-05-24 20:17:04,583][2737046] Updated weights for policy 0, policy_version 480 (0.0008)
251
+ [2023-05-24 20:17:05,976][2722668] Fps is (10 sec: 22527.9, 60 sec: 22459.7, 300 sec: 20997.4). Total num frames: 1994752. Throughput: 0: 5617.1. Samples: 491246. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
252
+ [2023-05-24 20:17:05,977][2722668] Avg episode reward: [(0, '11.487')]
253
+ [2023-05-24 20:17:06,386][2737046] Updated weights for policy 0, policy_version 490 (0.0009)
254
+ [2023-05-24 20:17:08,197][2737046] Updated weights for policy 0, policy_version 500 (0.0008)
255
+ [2023-05-24 20:17:10,043][2737046] Updated weights for policy 0, policy_version 510 (0.0010)
256
+ [2023-05-24 20:17:10,976][2722668] Fps is (10 sec: 22528.0, 60 sec: 22459.7, 300 sec: 21094.4). Total num frames: 2109440. Throughput: 0: 5627.3. Samples: 525054. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
257
+ [2023-05-24 20:17:10,977][2722668] Avg episode reward: [(0, '13.819')]
258
+ [2023-05-24 20:17:10,982][2737021] Saving new best policy, reward=13.819!
259
+ [2023-05-24 20:17:11,882][2737046] Updated weights for policy 0, policy_version 520 (0.0009)
260
+ [2023-05-24 20:17:13,742][2737046] Updated weights for policy 0, policy_version 530 (0.0009)
261
+ [2023-05-24 20:17:15,605][2737046] Updated weights for policy 0, policy_version 540 (0.0009)
262
+ [2023-05-24 20:17:15,976][2722668] Fps is (10 sec: 22527.9, 60 sec: 22459.7, 300 sec: 21143.2). Total num frames: 2220032. Throughput: 0: 5625.3. Samples: 541624. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
263
+ [2023-05-24 20:17:15,977][2722668] Avg episode reward: [(0, '14.199')]
264
+ [2023-05-24 20:17:15,979][2737021] Saving new best policy, reward=14.199!
265
+ [2023-05-24 20:17:17,499][2737046] Updated weights for policy 0, policy_version 550 (0.0009)
266
+ [2023-05-24 20:17:19,360][2737046] Updated weights for policy 0, policy_version 560 (0.0009)
267
+ [2023-05-24 20:17:20,976][2722668] Fps is (10 sec: 21708.6, 60 sec: 22391.4, 300 sec: 21150.2). Total num frames: 2326528. Throughput: 0: 5604.8. Samples: 574462. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
268
+ [2023-05-24 20:17:20,977][2722668] Avg episode reward: [(0, '18.293')]
269
+ [2023-05-24 20:17:20,997][2737021] Saving /home/mark/rl_course/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000569_2330624.pth...
270
+ [2023-05-24 20:17:21,040][2737021] Saving new best policy, reward=18.293!
271
+ [2023-05-24 20:17:21,192][2737046] Updated weights for policy 0, policy_version 570 (0.0008)
272
+ [2023-05-24 20:17:23,003][2737046] Updated weights for policy 0, policy_version 580 (0.0009)
273
+ [2023-05-24 20:17:24,820][2737046] Updated weights for policy 0, policy_version 590 (0.0009)
274
+ [2023-05-24 20:17:25,976][2722668] Fps is (10 sec: 22118.5, 60 sec: 22459.8, 300 sec: 21228.0). Total num frames: 2441216. Throughput: 0: 5604.1. Samples: 608214. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
275
+ [2023-05-24 20:17:25,977][2722668] Avg episode reward: [(0, '17.900')]
276
+ [2023-05-24 20:17:26,623][2737046] Updated weights for policy 0, policy_version 600 (0.0008)
277
+ [2023-05-24 20:17:28,477][2737046] Updated weights for policy 0, policy_version 610 (0.0009)
278
+ [2023-05-24 20:17:30,310][2737046] Updated weights for policy 0, policy_version 620 (0.0009)
279
+ [2023-05-24 20:17:30,976][2722668] Fps is (10 sec: 22528.1, 60 sec: 22459.7, 300 sec: 21265.1). Total num frames: 2551808. Throughput: 0: 5600.6. Samples: 624990. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
280
+ [2023-05-24 20:17:30,977][2722668] Avg episode reward: [(0, '19.475')]
281
+ [2023-05-24 20:17:30,983][2737021] Saving new best policy, reward=19.475!
282
+ [2023-05-24 20:17:32,166][2737046] Updated weights for policy 0, policy_version 630 (0.0009)
283
+ [2023-05-24 20:17:34,004][2737046] Updated weights for policy 0, policy_version 640 (0.0008)
284
+ [2023-05-24 20:17:35,838][2737046] Updated weights for policy 0, policy_version 650 (0.0009)
285
+ [2023-05-24 20:17:35,976][2722668] Fps is (10 sec: 22118.5, 60 sec: 22391.5, 300 sec: 21299.2). Total num frames: 2662400. Throughput: 0: 5596.0. Samples: 658444. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
286
+ [2023-05-24 20:17:35,977][2722668] Avg episode reward: [(0, '20.309')]
287
+ [2023-05-24 20:17:35,978][2737021] Saving new best policy, reward=20.309!
288
+ [2023-05-24 20:17:37,680][2737046] Updated weights for policy 0, policy_version 660 (0.0009)
289
+ [2023-05-24 20:17:39,524][2737046] Updated weights for policy 0, policy_version 670 (0.0009)
290
+ [2023-05-24 20:17:40,976][2722668] Fps is (10 sec: 22118.6, 60 sec: 22391.5, 300 sec: 21330.7). Total num frames: 2772992. Throughput: 0: 5591.3. Samples: 692022. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
291
+ [2023-05-24 20:17:40,977][2722668] Avg episode reward: [(0, '20.988')]
292
+ [2023-05-24 20:17:40,984][2737021] Saving new best policy, reward=20.988!
293
+ [2023-05-24 20:17:41,339][2737046] Updated weights for policy 0, policy_version 680 (0.0008)
294
+ [2023-05-24 20:17:43,169][2737046] Updated weights for policy 0, policy_version 690 (0.0008)
295
+ [2023-05-24 20:17:44,986][2737046] Updated weights for policy 0, policy_version 700 (0.0009)
296
+ [2023-05-24 20:17:45,976][2722668] Fps is (10 sec: 22528.0, 60 sec: 22391.5, 300 sec: 21390.2). Total num frames: 2887680. Throughput: 0: 5590.0. Samples: 708872. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
297
+ [2023-05-24 20:17:45,977][2722668] Avg episode reward: [(0, '23.068')]
298
+ [2023-05-24 20:17:45,978][2737021] Saving new best policy, reward=23.068!
299
+ [2023-05-24 20:17:46,819][2737046] Updated weights for policy 0, policy_version 710 (0.0008)
300
+ [2023-05-24 20:17:48,686][2737046] Updated weights for policy 0, policy_version 720 (0.0008)
301
+ [2023-05-24 20:17:50,505][2737046] Updated weights for policy 0, policy_version 730 (0.0008)
302
+ [2023-05-24 20:17:50,976][2722668] Fps is (10 sec: 22528.0, 60 sec: 22391.5, 300 sec: 21416.2). Total num frames: 2998272. Throughput: 0: 5580.3. Samples: 742360. Policy #0 lag: (min: 0.0, avg: 0.8, max: 1.0)
303
+ [2023-05-24 20:17:50,977][2722668] Avg episode reward: [(0, '21.517')]
304
+ [2023-05-24 20:17:52,347][2737046] Updated weights for policy 0, policy_version 740 (0.0009)
305
+ [2023-05-24 20:17:54,179][2737046] Updated weights for policy 0, policy_version 750 (0.0009)
306
+ [2023-05-24 20:17:55,976][2722668] Fps is (10 sec: 22118.3, 60 sec: 22323.2, 300 sec: 21440.4). Total num frames: 3108864. Throughput: 0: 5577.1. Samples: 776024. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
307
+ [2023-05-24 20:17:55,977][2722668] Avg episode reward: [(0, '23.550')]
308
+ [2023-05-24 20:17:55,992][2737021] Saving new best policy, reward=23.550!
309
+ [2023-05-24 20:17:55,992][2737046] Updated weights for policy 0, policy_version 760 (0.0009)
310
+ [2023-05-24 20:17:57,814][2737046] Updated weights for policy 0, policy_version 770 (0.0009)
311
+ [2023-05-24 20:17:59,632][2737046] Updated weights for policy 0, policy_version 780 (0.0009)
312
+ [2023-05-24 20:18:00,976][2722668] Fps is (10 sec: 22527.9, 60 sec: 22323.2, 300 sec: 21490.4). Total num frames: 3223552. Throughput: 0: 5583.7. Samples: 792890. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
313
+ [2023-05-24 20:18:00,977][2722668] Avg episode reward: [(0, '26.290')]
314
+ [2023-05-24 20:18:00,982][2737021] Saving new best policy, reward=26.290!
315
+ [2023-05-24 20:18:01,438][2737046] Updated weights for policy 0, policy_version 790 (0.0009)
316
+ [2023-05-24 20:18:03,272][2737046] Updated weights for policy 0, policy_version 800 (0.0008)
317
+ [2023-05-24 20:18:05,079][2737046] Updated weights for policy 0, policy_version 810 (0.0009)
318
+ [2023-05-24 20:18:05,976][2722668] Fps is (10 sec: 22528.0, 60 sec: 22323.2, 300 sec: 21510.6). Total num frames: 3334144. Throughput: 0: 5604.7. Samples: 826672. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0)
319
+ [2023-05-24 20:18:05,977][2722668] Avg episode reward: [(0, '22.770')]
320
+ [2023-05-24 20:18:06,915][2737046] Updated weights for policy 0, policy_version 820 (0.0009)
321
+ [2023-05-24 20:18:08,737][2737046] Updated weights for policy 0, policy_version 830 (0.0009)
322
+ [2023-05-24 20:18:10,541][2737046] Updated weights for policy 0, policy_version 840 (0.0009)
323
+ [2023-05-24 20:18:10,976][2722668] Fps is (10 sec: 22528.0, 60 sec: 22323.2, 300 sec: 21555.2). Total num frames: 3448832. Throughput: 0: 5604.0. Samples: 860396. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
324
+ [2023-05-24 20:18:10,977][2722668] Avg episode reward: [(0, '22.683')]
325
+ [2023-05-24 20:18:12,338][2737046] Updated weights for policy 0, policy_version 850 (0.0008)
326
+ [2023-05-24 20:18:14,145][2737046] Updated weights for policy 0, policy_version 860 (0.0008)
327
+ [2023-05-24 20:18:15,953][2737046] Updated weights for policy 0, policy_version 870 (0.0009)
328
+ [2023-05-24 20:18:15,976][2722668] Fps is (10 sec: 22937.7, 60 sec: 22391.5, 300 sec: 21597.1). Total num frames: 3563520. Throughput: 0: 5608.9. Samples: 877388. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
329
+ [2023-05-24 20:18:15,977][2722668] Avg episode reward: [(0, '22.152')]
330
+ [2023-05-24 20:18:17,733][2737046] Updated weights for policy 0, policy_version 880 (0.0008)
331
+ [2023-05-24 20:18:19,538][2737046] Updated weights for policy 0, policy_version 890 (0.0008)
332
+ [2023-05-24 20:18:20,976][2722668] Fps is (10 sec: 22528.0, 60 sec: 22459.8, 300 sec: 21612.4). Total num frames: 3674112. Throughput: 0: 5625.5. Samples: 911592. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
333
+ [2023-05-24 20:18:20,977][2722668] Avg episode reward: [(0, '22.559')]
334
+ [2023-05-24 20:18:21,328][2737046] Updated weights for policy 0, policy_version 900 (0.0008)
335
+ [2023-05-24 20:18:23,130][2737046] Updated weights for policy 0, policy_version 910 (0.0009)
336
+ [2023-05-24 20:18:24,913][2737046] Updated weights for policy 0, policy_version 920 (0.0009)
337
+ [2023-05-24 20:18:25,976][2722668] Fps is (10 sec: 22527.9, 60 sec: 22459.7, 300 sec: 21650.3). Total num frames: 3788800. Throughput: 0: 5641.0. Samples: 945866. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
338
+ [2023-05-24 20:18:25,977][2722668] Avg episode reward: [(0, '24.137')]
339
+ [2023-05-24 20:18:26,715][2737046] Updated weights for policy 0, policy_version 930 (0.0008)
340
+ [2023-05-24 20:18:28,514][2737046] Updated weights for policy 0, policy_version 940 (0.0009)
341
+ [2023-05-24 20:18:30,317][2737046] Updated weights for policy 0, policy_version 950 (0.0008)
342
+ [2023-05-24 20:18:30,976][2722668] Fps is (10 sec: 22937.5, 60 sec: 22528.0, 300 sec: 21686.0). Total num frames: 3903488. Throughput: 0: 5646.4. Samples: 962962. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
343
+ [2023-05-24 20:18:30,977][2722668] Avg episode reward: [(0, '25.558')]
344
+ [2023-05-24 20:18:32,129][2737046] Updated weights for policy 0, policy_version 960 (0.0009)
345
+ [2023-05-24 20:18:33,905][2737046] Updated weights for policy 0, policy_version 970 (0.0008)
346
+ [2023-05-24 20:18:35,357][2737021] Stopping Batcher_0...
347
+ [2023-05-24 20:18:35,357][2737021] Saving /home/mark/rl_course/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
348
+ [2023-05-24 20:18:35,358][2737021] Loop batcher_evt_loop terminating...
349
+ [2023-05-24 20:18:35,365][2722668] Component Batcher_0 stopped!
350
+ [2023-05-24 20:18:35,370][2737054] Stopping RolloutWorker_w7...
351
+ [2023-05-24 20:18:35,370][2737054] Loop rollout_proc7_evt_loop terminating...
352
+ [2023-05-24 20:18:35,370][2737050] Stopping RolloutWorker_w3...
353
+ [2023-05-24 20:18:35,371][2737049] Stopping RolloutWorker_w0...
354
+ [2023-05-24 20:18:35,371][2737050] Loop rollout_proc3_evt_loop terminating...
355
+ [2023-05-24 20:18:35,371][2737049] Loop rollout_proc0_evt_loop terminating...
356
+ [2023-05-24 20:18:35,370][2722668] Component RolloutWorker_w7 stopped!
357
+ [2023-05-24 20:18:35,371][2737052] Stopping RolloutWorker_w4...
358
+ [2023-05-24 20:18:35,371][2737047] Stopping RolloutWorker_w2...
359
+ [2023-05-24 20:18:35,371][2737052] Loop rollout_proc4_evt_loop terminating...
360
+ [2023-05-24 20:18:35,372][2737047] Loop rollout_proc2_evt_loop terminating...
361
+ [2023-05-24 20:18:35,372][2737046] Weights refcount: 2 0
362
+ [2023-05-24 20:18:35,372][2737051] Stopping RolloutWorker_w5...
363
+ [2023-05-24 20:18:35,372][2737048] Stopping RolloutWorker_w1...
364
+ [2023-05-24 20:18:35,372][2722668] Component RolloutWorker_w3 stopped!
365
+ [2023-05-24 20:18:35,372][2737051] Loop rollout_proc5_evt_loop terminating...
366
+ [2023-05-24 20:18:35,372][2737048] Loop rollout_proc1_evt_loop terminating...
367
+ [2023-05-24 20:18:35,372][2722668] Component RolloutWorker_w0 stopped!
368
+ [2023-05-24 20:18:35,373][2737046] Stopping InferenceWorker_p0-w0...
369
+ [2023-05-24 20:18:35,373][2737046] Loop inference_proc0-0_evt_loop terminating...
370
+ [2023-05-24 20:18:35,373][2737053] Stopping RolloutWorker_w6...
371
+ [2023-05-24 20:18:35,374][2737053] Loop rollout_proc6_evt_loop terminating...
372
+ [2023-05-24 20:18:35,373][2722668] Component RolloutWorker_w4 stopped!
373
+ [2023-05-24 20:18:35,375][2722668] Component RolloutWorker_w2 stopped!
374
+ [2023-05-24 20:18:35,376][2722668] Component RolloutWorker_w5 stopped!
375
+ [2023-05-24 20:18:35,377][2722668] Component RolloutWorker_w1 stopped!
376
+ [2023-05-24 20:18:35,377][2722668] Component InferenceWorker_p0-w0 stopped!
377
+ [2023-05-24 20:18:35,378][2722668] Component RolloutWorker_w6 stopped!
378
+ [2023-05-24 20:18:35,414][2737021] Saving /home/mark/rl_course/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
379
+ [2023-05-24 20:18:35,481][2737021] Stopping LearnerWorker_p0...
380
+ [2023-05-24 20:18:35,481][2737021] Loop learner_proc0_evt_loop terminating...
381
+ [2023-05-24 20:18:35,481][2722668] Component LearnerWorker_p0 stopped!
382
+ [2023-05-24 20:18:35,483][2722668] Waiting for process learner_proc0 to stop...
383
+ [2023-05-24 20:18:36,292][2722668] Waiting for process inference_proc0-0 to join...
384
+ [2023-05-24 20:18:36,294][2722668] Waiting for process rollout_proc0 to join...
385
+ [2023-05-24 20:18:36,296][2722668] Waiting for process rollout_proc1 to join...
386
+ [2023-05-24 20:18:36,297][2722668] Waiting for process rollout_proc2 to join...
387
+ [2023-05-24 20:18:36,298][2722668] Waiting for process rollout_proc3 to join...
388
+ [2023-05-24 20:18:36,300][2722668] Waiting for process rollout_proc4 to join...
389
+ [2023-05-24 20:18:36,301][2722668] Waiting for process rollout_proc5 to join...
390
+ [2023-05-24 20:18:36,302][2722668] Waiting for process rollout_proc6 to join...
391
+ [2023-05-24 20:18:36,303][2722668] Waiting for process rollout_proc7 to join...
392
+ [2023-05-24 20:18:36,304][2722668] Batcher 0 profile tree view:
393
+ batching: 12.0222, releasing_batches: 0.0254
394
+ [2023-05-24 20:18:36,305][2722668] InferenceWorker_p0-w0 profile tree view:
395
+ wait_policy: 0.0000
396
+ wait_policy_total: 5.1293
397
+ update_model: 2.8909
398
+ weight_update: 0.0008
399
+ one_step: 0.0017
400
+ handle_policy_step: 163.8550
401
+ deserialize: 6.4795, stack: 0.9489, obs_to_device_normalize: 40.6929, forward: 70.6622, send_messages: 10.9683
402
+ prepare_outputs: 26.5535
403
+ to_cpu: 17.5240
404
+ [2023-05-24 20:18:36,306][2722668] Learner 0 profile tree view:
405
+ misc: 0.0045, prepare_batch: 8.7858
406
+ train: 24.3247
407
+ epoch_init: 0.0055, minibatch_init: 0.0054, losses_postprocess: 0.2306, kl_divergence: 0.2157, after_optimizer: 7.1346
408
+ calculate_losses: 8.0696
409
+ losses_init: 0.0036, forward_head: 0.7724, bptt_initial: 4.9783, tail: 0.4018, advantages_returns: 0.1117, losses: 0.8255
410
+ bptt: 0.8351
411
+ bptt_forward_core: 0.8008
412
+ update: 8.3318
413
+ clip: 1.1532
414
+ [2023-05-24 20:18:36,306][2722668] RolloutWorker_w0 profile tree view:
415
+ wait_for_trajectories: 0.1621, enqueue_policy_requests: 7.3629, env_step: 118.9770, overhead: 8.9700, complete_rollouts: 0.2239
416
+ save_policy_outputs: 8.9400
417
+ split_output_tensors: 4.3843
418
+ [2023-05-24 20:18:36,307][2722668] RolloutWorker_w7 profile tree view:
419
+ wait_for_trajectories: 0.1557, enqueue_policy_requests: 7.3900, env_step: 118.9123, overhead: 9.0051, complete_rollouts: 0.2221
420
+ save_policy_outputs: 9.1419
421
+ split_output_tensors: 4.4803
422
+ [2023-05-24 20:18:36,308][2722668] Loop Runner_EvtLoop terminating...
423
+ [2023-05-24 20:18:36,309][2722668] Runner profile tree view:
424
+ main_loop: 191.8832
425
+ [2023-05-24 20:18:36,310][2722668] Collected {0: 4005888}, FPS: 20876.7
426
+ [2023-05-24 20:25:41,995][2722668] Loading existing experiment configuration from /home/mark/rl_course/unit8/train_dir/default_experiment/config.json
427
+ [2023-05-24 20:25:41,996][2722668] Overriding arg 'num_workers' with value 1 passed from command line
428
+ [2023-05-24 20:25:41,997][2722668] Adding new argument 'no_render'=True that is not in the saved config file!
429
+ [2023-05-24 20:25:41,997][2722668] Adding new argument 'save_video'=True that is not in the saved config file!
430
+ [2023-05-24 20:25:41,998][2722668] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
431
+ [2023-05-24 20:25:41,999][2722668] Adding new argument 'video_name'=None that is not in the saved config file!
432
+ [2023-05-24 20:25:41,999][2722668] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file!
433
+ [2023-05-24 20:25:42,000][2722668] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
434
+ [2023-05-24 20:25:42,001][2722668] Adding new argument 'push_to_hub'=False that is not in the saved config file!
435
+ [2023-05-24 20:25:42,001][2722668] Adding new argument 'hf_repository'=None that is not in the saved config file!
436
+ [2023-05-24 20:25:42,002][2722668] Adding new argument 'policy_index'=0 that is not in the saved config file!
437
+ [2023-05-24 20:25:42,003][2722668] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
438
+ [2023-05-24 20:25:42,004][2722668] Adding new argument 'train_script'=None that is not in the saved config file!
439
+ [2023-05-24 20:25:42,004][2722668] Adding new argument 'enjoy_script'=None that is not in the saved config file!
440
+ [2023-05-24 20:25:42,007][2722668] Using frameskip 1 and render_action_repeat=4 for evaluation
441
+ [2023-05-24 20:25:42,019][2722668] Doom resolution: 160x120, resize resolution: (128, 72)
442
+ [2023-05-24 20:25:42,021][2722668] RunningMeanStd input shape: (3, 72, 128)
443
+ [2023-05-24 20:25:42,023][2722668] RunningMeanStd input shape: (1,)
444
+ [2023-05-24 20:25:42,047][2722668] ConvEncoder: input_channels=3
445
+ [2023-05-24 20:25:42,207][2722668] Conv encoder output size: 512
446
+ [2023-05-24 20:25:42,208][2722668] Policy head output size: 512
447
+ [2023-05-24 20:25:44,701][2722668] Loading state from checkpoint /home/mark/rl_course/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
448
+ [2023-05-24 20:25:46,226][2722668] Num frames 100...
449
+ [2023-05-24 20:25:46,388][2722668] Num frames 200...
450
+ [2023-05-24 20:25:46,546][2722668] Num frames 300...
451
+ [2023-05-24 20:25:46,704][2722668] Num frames 400...
452
+ [2023-05-24 20:25:46,870][2722668] Num frames 500...
453
+ [2023-05-24 20:25:47,030][2722668] Num frames 600...
454
+ [2023-05-24 20:25:47,200][2722668] Num frames 700...
455
+ [2023-05-24 20:25:47,369][2722668] Num frames 800...
456
+ [2023-05-24 20:25:47,528][2722668] Num frames 900...
457
+ [2023-05-24 20:25:47,694][2722668] Num frames 1000...
458
+ [2023-05-24 20:25:47,853][2722668] Num frames 1100...
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+ [2023-05-24 20:25:48,018][2722668] Num frames 1200...
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+ [2023-05-24 20:25:48,181][2722668] Num frames 1300...
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+ [2023-05-24 20:25:48,342][2722668] Num frames 1400...
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+ [2023-05-24 20:25:48,512][2722668] Num frames 1500...
463
+ [2023-05-24 20:25:48,684][2722668] Num frames 1600...
464
+ [2023-05-24 20:25:48,870][2722668] Avg episode rewards: #0: 39.780, true rewards: #0: 16.780
465
+ [2023-05-24 20:25:48,872][2722668] Avg episode reward: 39.780, avg true_objective: 16.780
466
+ [2023-05-24 20:25:48,913][2722668] Num frames 1700...
467
+ [2023-05-24 20:25:49,081][2722668] Num frames 1800...
468
+ [2023-05-24 20:25:49,236][2722668] Num frames 1900...
469
+ [2023-05-24 20:25:49,394][2722668] Num frames 2000...
470
+ [2023-05-24 20:25:49,553][2722668] Num frames 2100...
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+ [2023-05-24 20:25:49,720][2722668] Num frames 2200...
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+ [2023-05-24 20:25:49,885][2722668] Num frames 2300...
473
+ [2023-05-24 20:25:50,050][2722668] Num frames 2400...
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+ [2023-05-24 20:25:50,215][2722668] Num frames 2500...
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+ [2023-05-24 20:25:50,629][2722668] Avg episode rewards: #0: 30.640, true rewards: #0: 13.640
478
+ [2023-05-24 20:25:50,631][2722668] Avg episode reward: 30.640, avg true_objective: 13.640
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+ [2023-05-24 20:25:50,749][2722668] Num frames 2800...
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+ [2023-05-24 20:25:51,283][2722668] Avg episode rewards: #0: 22.010, true rewards: #0: 10.343
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+ [2023-05-24 20:25:51,284][2722668] Avg episode reward: 22.010, avg true_objective: 10.343
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+ [2023-05-24 20:25:51,452][2722668] Num frames 3200...
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+ [2023-05-24 20:25:53,575][2722668] Avg episode rewards: #0: 23.368, true rewards: #0: 11.117
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+ [2023-05-24 20:25:53,577][2722668] Avg episode reward: 23.368, avg true_objective: 11.117
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+ [2023-05-24 20:25:53,670][2722668] Num frames 4500...
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+ [2023-05-24 20:25:54,978][2722668] Num frames 5300...
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+ [2023-05-24 20:25:55,055][2722668] Avg episode rewards: #0: 22.222, true rewards: #0: 10.622
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+ [2023-05-24 20:25:55,057][2722668] Avg episode reward: 22.222, avg true_objective: 10.622
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+ [2023-05-24 20:25:55,204][2722668] Num frames 5400...
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+ [2023-05-24 20:25:56,624][2722668] Num frames 6300...
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+ [2023-05-24 20:25:56,686][2722668] Avg episode rewards: #0: 22.172, true rewards: #0: 10.505
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+ [2023-05-24 20:25:56,688][2722668] Avg episode reward: 22.172, avg true_objective: 10.505
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+ [2023-05-24 20:25:56,845][2722668] Num frames 6400...
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+ [2023-05-24 20:25:58,254][2722668] Num frames 7300...
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+ [2023-05-24 20:25:58,356][2722668] Avg episode rewards: #0: 22.469, true rewards: #0: 10.469
534
+ [2023-05-24 20:25:58,358][2722668] Avg episode reward: 22.469, avg true_objective: 10.469
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+ [2023-05-24 20:25:58,478][2722668] Num frames 7400...
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+ [2023-05-24 20:25:59,413][2722668] Avg episode rewards: #0: 21.039, true rewards: #0: 9.914
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+ [2023-05-24 20:25:59,415][2722668] Avg episode reward: 21.039, avg true_objective: 9.914
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+ [2023-05-24 20:25:59,534][2722668] Num frames 8000...
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+ [2023-05-24 20:26:00,065][2722668] Avg episode rewards: #0: 19.491, true rewards: #0: 9.269
548
+ [2023-05-24 20:26:00,067][2722668] Avg episode reward: 19.491, avg true_objective: 9.269
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+ [2023-05-24 20:26:00,151][2722668] Num frames 8400...
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+ [2023-05-24 20:26:02,736][2722668] Num frames 10000...
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+ [2023-05-24 20:26:03,073][2722668] Num frames 10200...
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+ [2023-05-24 20:26:03,243][2722668] Num frames 10300...
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+ [2023-05-24 20:26:03,442][2722668] Avg episode rewards: #0: 23.177, true rewards: #0: 10.377
570
+ [2023-05-24 20:26:03,443][2722668] Avg episode reward: 23.177, avg true_objective: 10.377
571
+ [2023-05-24 20:26:28,662][2722668] Replay video saved to /home/mark/rl_course/unit8/train_dir/default_experiment/replay.mp4!
572
+ [2023-05-24 20:36:55,928][2722668] Loading existing experiment configuration from /home/mark/rl_course/unit8/train_dir/default_experiment/config.json
573
+ [2023-05-24 20:36:55,929][2722668] Overriding arg 'num_workers' with value 1 passed from command line
574
+ [2023-05-24 20:36:55,930][2722668] Adding new argument 'no_render'=True that is not in the saved config file!
575
+ [2023-05-24 20:36:55,931][2722668] Adding new argument 'save_video'=True that is not in the saved config file!
576
+ [2023-05-24 20:36:55,931][2722668] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
577
+ [2023-05-24 20:36:55,932][2722668] Adding new argument 'video_name'=None that is not in the saved config file!
578
+ [2023-05-24 20:36:55,933][2722668] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!
579
+ [2023-05-24 20:36:55,935][2722668] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
580
+ [2023-05-24 20:36:55,935][2722668] Adding new argument 'push_to_hub'=True that is not in the saved config file!
581
+ [2023-05-24 20:36:55,936][2722668] Adding new argument 'hf_repository'='markeidsaune/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file!
582
+ [2023-05-24 20:36:55,937][2722668] Adding new argument 'policy_index'=0 that is not in the saved config file!
583
+ [2023-05-24 20:36:55,938][2722668] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
584
+ [2023-05-24 20:36:55,939][2722668] Adding new argument 'train_script'=None that is not in the saved config file!
585
+ [2023-05-24 20:36:55,940][2722668] Adding new argument 'enjoy_script'=None that is not in the saved config file!
586
+ [2023-05-24 20:36:55,942][2722668] Using frameskip 1 and render_action_repeat=4 for evaluation
587
+ [2023-05-24 20:36:55,956][2722668] RunningMeanStd input shape: (3, 72, 128)
588
+ [2023-05-24 20:36:55,958][2722668] RunningMeanStd input shape: (1,)
589
+ [2023-05-24 20:36:55,973][2722668] ConvEncoder: input_channels=3
590
+ [2023-05-24 20:36:56,024][2722668] Conv encoder output size: 512
591
+ [2023-05-24 20:36:56,025][2722668] Policy head output size: 512
592
+ [2023-05-24 20:36:56,073][2722668] Loading state from checkpoint /home/mark/rl_course/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
593
+ [2023-05-24 20:36:56,899][2722668] Num frames 100...
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+ [2023-05-24 20:36:57,069][2722668] Num frames 200...
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+ [2023-05-24 20:36:57,538][2722668] Num frames 500...
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+ [2023-05-24 20:36:57,632][2722668] Avg episode rewards: #0: 9.230, true rewards: #0: 5.230
599
+ [2023-05-24 20:36:57,634][2722668] Avg episode reward: 9.230, avg true_objective: 5.230
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+ [2023-05-24 20:36:57,756][2722668] Num frames 600...
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+ [2023-05-24 20:36:58,858][2722668] Num frames 1300...
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+ [2023-05-24 20:36:59,059][2722668] Avg episode rewards: #0: 14.435, true rewards: #0: 6.935
609
+ [2023-05-24 20:36:59,061][2722668] Avg episode reward: 14.435, avg true_objective: 6.935
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+ [2023-05-24 20:36:59,088][2722668] Num frames 1400...
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+ [2023-05-24 20:36:59,736][2722668] Num frames 1800...
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+ [2023-05-24 20:36:59,799][2722668] Avg episode rewards: #0: 11.677, true rewards: #0: 6.010
616
+ [2023-05-24 20:36:59,800][2722668] Avg episode reward: 11.677, avg true_objective: 6.010
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+ [2023-05-24 20:36:59,961][2722668] Num frames 1900...
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+ [2023-05-24 20:37:01,651][2722668] Num frames 3000...
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+ [2023-05-24 20:37:01,757][2722668] Avg episode rewards: #0: 15.830, true rewards: #0: 7.580
630
+ [2023-05-24 20:37:01,759][2722668] Avg episode reward: 15.830, avg true_objective: 7.580
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+ [2023-05-24 20:37:03,980][2722668] Num frames 4400...
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+ [2023-05-24 20:37:04,057][2722668] Avg episode rewards: #0: 18.616, true rewards: #0: 8.816
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+ [2023-05-24 20:37:04,058][2722668] Avg episode reward: 18.616, avg true_objective: 8.816
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+ [2023-05-24 20:37:04,219][2722668] Num frames 4500...
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+ [2023-05-24 20:37:05,198][2722668] Num frames 5100...
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+ [2023-05-24 20:37:05,274][2722668] Avg episode rewards: #0: 18.187, true rewards: #0: 8.520
655
+ [2023-05-24 20:37:05,276][2722668] Avg episode reward: 18.187, avg true_objective: 8.520
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+ [2023-05-24 20:37:05,417][2722668] Num frames 5200...
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+ [2023-05-24 20:37:08,180][2722668] Num frames 6900...
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+ [2023-05-24 20:37:08,296][2722668] Avg episode rewards: #0: 22.194, true rewards: #0: 9.909
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+ [2023-05-24 20:37:08,298][2722668] Avg episode reward: 22.194, avg true_objective: 9.909
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+ [2023-05-24 20:37:08,399][2722668] Num frames 7000...
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+ [2023-05-24 20:37:08,936][2722668] Avg episode rewards: #0: 20.373, true rewards: #0: 9.122
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+ [2023-05-24 20:37:08,938][2722668] Avg episode reward: 20.373, avg true_objective: 9.122
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+ [2023-05-24 20:37:08,947][2722668] Num frames 7300...
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+ [2023-05-24 20:37:10,079][2722668] Num frames 8000...
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+ [2023-05-24 20:37:11,027][2722668] Num frames 8600...
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+ [2023-05-24 20:37:11,193][2722668] Num frames 8700...
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+ [2023-05-24 20:37:11,356][2722668] Avg episode rewards: #0: 22.078, true rewards: #0: 9.744
697
+ [2023-05-24 20:37:11,358][2722668] Avg episode reward: 22.078, avg true_objective: 9.744
698
+ [2023-05-24 20:37:11,408][2722668] Num frames 8800...
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+ [2023-05-24 20:37:12,051][2722668] Num frames 9200...
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+ [2023-05-24 20:37:12,203][2722668] Num frames 9300...
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+ [2023-05-24 20:37:12,284][2722668] Avg episode rewards: #0: 20.614, true rewards: #0: 9.314
705
+ [2023-05-24 20:37:12,285][2722668] Avg episode reward: 20.614, avg true_objective: 9.314
706
+ [2023-05-24 20:37:34,803][2722668] Replay video saved to /home/mark/rl_course/unit8/train_dir/default_experiment/replay.mp4!