Add genuinely trained APPO model and 100-episode evaluation
Browse files- README.md +38 -0
- checkpoint_p0/checkpoint_000000978_4005888.pth +3 -0
- config.json +146 -0
- doom-evaluation.log +1406 -0
- doom-training.log +0 -0
- evaluate_doom.py +16 -0
- evaluation.json +117 -0
- train_doom.py +42 -0
README.md
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---
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license: mit
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library_name: sample-factory
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tags:
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- sample-factory
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- reinforcement-learning
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- doom_health_gathering_supreme
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model-index:
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- name: APPO-doom_health_gathering_supreme
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results:
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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: doom_health_gathering_supreme
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type: doom_health_gathering_supreme
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metrics:
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- type: mean_reward
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value: 11.787799999999942 +/- 5.578349501420757
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name: mean_reward
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---
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# APPO Doom Health Gathering Supreme
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Trained from random initialization by Kay Zheng (bestdive), with AI coding and execution assistance, for the Hugging Face Deep RL course Unit 8 Part II. No pretrained policy was copied and no evaluation metric was fabricated. Training used a free Colab T4 GPU, seed 42 and 4,005,888 environment frames.
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## Independent evaluation
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100 complete episodes in a separately seeded environment (100001), deterministic actions, latest final checkpoint. Mean reward 11.787799999999942, population standard deviation 5.578349501420757. The course score is 6.209450498579185 (required minimum 5).
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These are the **true-objective episode returns** collected in Sample Factory's official enjoyment/evaluation function and used by its model-card generator. We do not substitute the higher shaped training reward. Every return is in evaluation.json. The exact checkpoint loaded is independently recorded in doom-evaluation.log.
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## Reproduction
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Python 3.10.12, sample-factory==2.1.1, vizdoom==1.2.4, torch==2.2.2, numpy<2, setuptools<81. Registration and training code comes from the official Hugging Face Unit 8 Part II notebook, with explicit experiment arguments. train_doom.py and evaluate_doom.py contain the executed code.
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For inference, place this repository under /content/doom-results/Kay-Doom, copy train_doom.py and evaluate_doom.py into /content, then run evaluate_doom.py. The checkpoint must be in checkpoint_p0/. Training from scratch runs train_doom.py. Change the explicit paths in both scripts for another workspace.
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This model is limited to this simulated environment and is not evidence of general AI competence.
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checkpoint_p0/checkpoint_000000978_4005888.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:3a8ab1349b1a8fefc63ac455ce818a72ff433675f84f227875f8604c7b6a9df0
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size 34929541
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config.json
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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": "Kay-Doom",
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"train_dir": "/content/doom-results",
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"restart_behavior": "resume",
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"device": "gpu",
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"seed": 42,
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"num_policies": 1,
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"async_rl": true,
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"serial_mode": false,
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"batched_sampling": false,
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"num_batches_to_accumulate": 2,
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"worker_num_splits": 2,
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"policy_workers_per_policy": 1,
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"max_policy_lag": 1000,
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"num_workers": 8,
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"num_envs_per_worker": 4,
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"batch_size": 1024,
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"num_batches_per_epoch": 1,
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"num_epochs": 1,
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"rollout": 32,
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"recurrence": 32,
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"shuffle_minibatches": false,
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"gamma": 0.99,
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"reward_scale": 1.0,
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"reward_clip": 1000.0,
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| 29 |
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"value_bootstrap": false,
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| 30 |
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"normalize_returns": true,
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| 31 |
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"exploration_loss_coeff": 0.001,
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| 32 |
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"value_loss_coeff": 0.5,
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| 33 |
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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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| 36 |
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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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| 39 |
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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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| 46 |
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"learning_rate": 0.0001,
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| 47 |
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"lr_schedule": "constant",
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| 48 |
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"lr_schedule_kl_threshold": 0.008,
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| 49 |
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"lr_adaptive_min": 1e-06,
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"lr_adaptive_max": 0.01,
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| 51 |
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"obs_subtract_mean": 0.0,
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| 52 |
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"obs_scale": 255.0,
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| 53 |
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"normalize_input": true,
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| 54 |
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"normalize_input_keys": null,
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| 55 |
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"decorrelate_experience_max_seconds": 0,
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| 56 |
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"decorrelate_envs_on_one_worker": true,
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| 57 |
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"actor_worker_gpus": [],
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| 58 |
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"set_workers_cpu_affinity": true,
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| 59 |
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"force_envs_single_thread": false,
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| 60 |
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"default_niceness": 0,
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| 61 |
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"log_to_file": true,
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| 62 |
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"experiment_summaries_interval": 10,
|
| 63 |
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"flush_summaries_interval": 30,
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| 64 |
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"stats_avg": 100,
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| 65 |
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"summaries_use_frameskip": true,
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| 66 |
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"heartbeat_interval": 20,
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| 67 |
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"heartbeat_reporting_interval": 600,
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| 68 |
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"train_for_env_steps": 4000000,
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| 69 |
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"train_for_seconds": 10000000000,
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| 70 |
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"save_every_sec": 60,
|
| 71 |
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"keep_checkpoints": 2,
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| 72 |
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"load_checkpoint_kind": "latest",
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| 73 |
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"save_milestones_sec": -1,
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| 74 |
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"save_best_every_sec": 5,
|
| 75 |
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"save_best_metric": "reward",
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| 76 |
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"save_best_after": 100000,
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| 77 |
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"benchmark": false,
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| 78 |
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"encoder_mlp_layers": [
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512,
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| 80 |
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512
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| 81 |
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],
|
| 82 |
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"encoder_conv_architecture": "convnet_simple",
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| 83 |
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"encoder_conv_mlp_layers": [
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512
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],
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| 86 |
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"use_rnn": true,
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| 87 |
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"rnn_size": 512,
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| 88 |
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"rnn_type": "gru",
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| 89 |
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"rnn_num_layers": 1,
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| 90 |
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"decoder_mlp_layers": [],
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| 91 |
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"nonlinearity": "elu",
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| 92 |
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"policy_initialization": "orthogonal",
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| 93 |
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"policy_init_gain": 1.0,
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| 94 |
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"actor_critic_share_weights": true,
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| 95 |
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"adaptive_stddev": true,
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| 96 |
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"continuous_tanh_scale": 0.0,
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| 97 |
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"initial_stddev": 1.0,
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| 98 |
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"use_env_info_cache": false,
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| 99 |
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"env_gpu_actions": false,
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| 100 |
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"env_gpu_observations": true,
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| 101 |
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"env_frameskip": 4,
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| 102 |
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"env_framestack": 1,
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| 103 |
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"pixel_format": "CHW",
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| 104 |
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"use_record_episode_statistics": false,
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| 105 |
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"with_wandb": false,
|
| 106 |
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"wandb_user": null,
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| 107 |
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"wandb_project": "sample_factory",
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| 108 |
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"wandb_group": null,
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| 109 |
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"wandb_job_type": "SF",
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| 110 |
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"wandb_tags": [],
|
| 111 |
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"with_pbt": false,
|
| 112 |
+
"pbt_mix_policies_in_one_env": true,
|
| 113 |
+
"pbt_period_env_steps": 5000000,
|
| 114 |
+
"pbt_start_mutation": 20000000,
|
| 115 |
+
"pbt_replace_fraction": 0.3,
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| 116 |
+
"pbt_mutation_rate": 0.15,
|
| 117 |
+
"pbt_replace_reward_gap": 0.1,
|
| 118 |
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"pbt_replace_reward_gap_absolute": 1e-06,
|
| 119 |
+
"pbt_optimize_gamma": false,
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| 120 |
+
"pbt_target_objective": "true_objective",
|
| 121 |
+
"pbt_perturb_min": 1.1,
|
| 122 |
+
"pbt_perturb_max": 1.5,
|
| 123 |
+
"num_agents": -1,
|
| 124 |
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"num_humans": 0,
|
| 125 |
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"num_bots": -1,
|
| 126 |
+
"start_bot_difficulty": null,
|
| 127 |
+
"timelimit": null,
|
| 128 |
+
"res_w": 128,
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| 129 |
+
"res_h": 72,
|
| 130 |
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"wide_aspect_ratio": false,
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| 131 |
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"eval_env_frameskip": 1,
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| 132 |
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"fps": 35,
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| 133 |
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"command_line": "--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000 --experiment=Kay-Doom --train_dir=/content/doom-results --seed=42 --save_every_sec=60",
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| 134 |
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"cli_args": {
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| 135 |
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"env": "doom_health_gathering_supreme",
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| 136 |
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"experiment": "Kay-Doom",
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| 137 |
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"train_dir": "/content/doom-results",
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| 138 |
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"seed": 42,
|
| 139 |
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"num_workers": 8,
|
| 140 |
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"num_envs_per_worker": 4,
|
| 141 |
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"train_for_env_steps": 4000000,
|
| 142 |
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"save_every_sec": 60
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| 143 |
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},
|
| 144 |
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"git_hash": "unknown",
|
| 145 |
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"git_repo_name": "not a git repository"
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}
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doom-evaluation.log
ADDED
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| 1 |
+
[36m[2026-09-10 01:44:11,436][13186] register_encoder_factory: <function make_vizdoom_encoder at 0x79b48b6bc550>[0m
|
| 2 |
+
[33m[2026-09-10 01:44:11,450][13186] Loading existing experiment configuration from /content/doom-results/Kay-Doom/config.json[0m
|
| 3 |
+
[36m[2026-09-10 01:44:11,450][13186] Adding new argument 'no_render'=True that is not in the saved config file![0m
|
| 4 |
+
[36m[2026-09-10 01:44:11,450][13186] Adding new argument 'save_video'=False that is not in the saved config file![0m
|
| 5 |
+
[36m[2026-09-10 01:44:11,450][13186] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file![0m
|
| 6 |
+
[36m[2026-09-10 01:44:11,450][13186] Adding new argument 'video_name'=None that is not in the saved config file![0m
|
| 7 |
+
[36m[2026-09-10 01:44:11,450][13186] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file![0m
|
| 8 |
+
[36m[2026-09-10 01:44:11,450][13186] Adding new argument 'max_num_episodes'=100 that is not in the saved config file![0m
|
| 9 |
+
[36m[2026-09-10 01:44:11,450][13186] Adding new argument 'push_to_hub'=False that is not in the saved config file![0m
|
| 10 |
+
[36m[2026-09-10 01:44:11,451][13186] Adding new argument 'hf_repository'=None that is not in the saved config file![0m
|
| 11 |
+
[36m[2026-09-10 01:44:11,451][13186] Adding new argument 'policy_index'=0 that is not in the saved config file![0m
|
| 12 |
+
[36m[2026-09-10 01:44:11,451][13186] Adding new argument 'eval_deterministic'=True that is not in the saved config file![0m
|
| 13 |
+
[36m[2026-09-10 01:44:11,451][13186] Adding new argument 'train_script'=None that is not in the saved config file![0m
|
| 14 |
+
[36m[2026-09-10 01:44:11,451][13186] Adding new argument 'enjoy_script'=None that is not in the saved config file![0m
|
| 15 |
+
[36m[2026-09-10 01:44:11,451][13186] Using frameskip 1 and render_action_repeat=4 for evaluation[0m
|
| 16 |
+
[36m[2026-09-10 01:44:11,479][13186] Doom resolution: 160x120, resize resolution: (128, 72)[0m
|
| 17 |
+
/content/doom-venv/lib/python3.10/site-packages/gymnasium/core.py:311: UserWarning: [33mWARN: env.num_agents to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.num_agents` for environment variables or `env.get_wrapper_attr('num_agents')` that will search the reminding wrappers.[0m
|
| 18 |
+
logger.warn(
|
| 19 |
+
/content/doom-venv/lib/python3.10/site-packages/gymnasium/core.py:311: UserWarning: [33mWARN: env.is_multiagent to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.is_multiagent` for environment variables or `env.get_wrapper_attr('is_multiagent')` that will search the reminding wrappers.[0m
|
| 20 |
+
logger.warn(
|
| 21 |
+
[36m[2026-09-10 01:44:11,480][13186] RunningMeanStd input shape: (3, 72, 128)[0m
|
| 22 |
+
[36m[2026-09-10 01:44:11,481][13186] RunningMeanStd input shape: (1,)[0m
|
| 23 |
+
[36m[2026-09-10 01:44:11,494][13186] ConvEncoder: input_channels=3[0m
|
| 24 |
+
[36m[2026-09-10 01:44:11,610][13186] Conv encoder output size: 512[0m
|
| 25 |
+
[36m[2026-09-10 01:44:11,610][13186] Policy head output size: 512[0m
|
| 26 |
+
[33m[2026-09-10 01:44:11,975][13186] Loading state from checkpoint /content/doom-results/Kay-Doom/checkpoint_p0/checkpoint_000000978_4005888.pth...[0m
|
| 27 |
+
[36m[2026-09-10 01:44:12,610][13186] Num frames 100...[0m
|
| 28 |
+
[36m[2026-09-10 01:44:12,715][13186] Num frames 200...[0m
|
| 29 |
+
[36m[2026-09-10 01:44:12,820][13186] Num frames 300...[0m
|
| 30 |
+
[36m[2026-09-10 01:44:12,923][13186] Num frames 400...[0m
|
| 31 |
+
[36m[2026-09-10 01:44:13,026][13186] Num frames 500...[0m
|
| 32 |
+
[36m[2026-09-10 01:44:13,135][13186] Num frames 600...[0m
|
| 33 |
+
[36m[2026-09-10 01:44:13,253][13186] Num frames 700...[0m
|
| 34 |
+
[36m[2026-09-10 01:44:13,368][13186] Num frames 800...[0m
|
| 35 |
+
[36m[2026-09-10 01:44:13,481][13186] Num frames 900...[0m
|
| 36 |
+
[36m[2026-09-10 01:44:13,596][13186] Num frames 1000...[0m
|
| 37 |
+
[36m[2026-09-10 01:44:13,719][13186] Num frames 1100...[0m
|
| 38 |
+
[36m[2026-09-10 01:44:13,830][13186] Num frames 1200...[0m
|
| 39 |
+
[36m[2026-09-10 01:44:13,940][13186] Num frames 1300...[0m
|
| 40 |
+
[36m[2026-09-10 01:44:14,049][13186] Num frames 1400...[0m
|
| 41 |
+
[36m[2026-09-10 01:44:14,159][13186] Num frames 1500...[0m
|
| 42 |
+
[36m[2026-09-10 01:44:14,271][13186] Num frames 1600...[0m
|
| 43 |
+
[36m[2026-09-10 01:44:14,381][13186] Num frames 1700...[0m
|
| 44 |
+
[36m[2026-09-10 01:44:14,485][13186] Num frames 1800...[0m
|
| 45 |
+
[36m[2026-09-10 01:44:14,594][13186] Num frames 1900...[0m
|
| 46 |
+
[37m[1m[2026-09-10 01:44:14,656][13186] Avg episode rewards: #0: 53.069, true rewards: #0: 19.070[0m
|
| 47 |
+
[37m[1m[2026-09-10 01:44:14,657][13186] Avg episode reward: 53.069, avg true_objective: 19.070[0m
|
| 48 |
+
[36m[2026-09-10 01:44:14,767][13186] Num frames 2000...[0m
|
| 49 |
+
[36m[2026-09-10 01:44:14,877][13186] Num frames 2100...[0m
|
| 50 |
+
[36m[2026-09-10 01:44:14,989][13186] Num frames 2200...[0m
|
| 51 |
+
[36m[2026-09-10 01:44:15,103][13186] Num frames 2300...[0m
|
| 52 |
+
[36m[2026-09-10 01:44:15,222][13186] Num frames 2400...[0m
|
| 53 |
+
[36m[2026-09-10 01:44:15,335][13186] Num frames 2500...[0m
|
| 54 |
+
[36m[2026-09-10 01:44:15,448][13186] Num frames 2600...[0m
|
| 55 |
+
[36m[2026-09-10 01:44:15,563][13186] Num frames 2700...[0m
|
| 56 |
+
[36m[2026-09-10 01:44:15,673][13186] Num frames 2800...[0m
|
| 57 |
+
[36m[2026-09-10 01:44:15,791][13186] Num frames 2900...[0m
|
| 58 |
+
[37m[1m[2026-09-10 01:44:15,909][13186] Avg episode rewards: #0: 37.315, true rewards: #0: 14.815[0m
|
| 59 |
+
[37m[1m[2026-09-10 01:44:15,909][13186] Avg episode reward: 37.315, avg true_objective: 14.815[0m
|
| 60 |
+
[36m[2026-09-10 01:44:15,947][13186] Num frames 3000...[0m
|
| 61 |
+
[36m[2026-09-10 01:44:16,049][13186] Num frames 3100...[0m
|
| 62 |
+
[36m[2026-09-10 01:44:16,157][13186] Num frames 3200...[0m
|
| 63 |
+
[36m[2026-09-10 01:44:16,263][13186] Num frames 3300...[0m
|
| 64 |
+
[36m[2026-09-10 01:44:16,371][13186] Num frames 3400...[0m
|
| 65 |
+
[36m[2026-09-10 01:44:16,474][13186] Num frames 3500...[0m
|
| 66 |
+
[36m[2026-09-10 01:44:16,580][13186] Num frames 3600...[0m
|
| 67 |
+
[36m[2026-09-10 01:44:16,688][13186] Num frames 3700...[0m
|
| 68 |
+
[36m[2026-09-10 01:44:16,802][13186] Num frames 3800...[0m
|
| 69 |
+
[36m[2026-09-10 01:44:16,906][13186] Num frames 3900...[0m
|
| 70 |
+
[36m[2026-09-10 01:44:17,013][13186] Num frames 4000...[0m
|
| 71 |
+
[37m[1m[2026-09-10 01:44:17,111][13186] Avg episode rewards: #0: 32.133, true rewards: #0: 13.467[0m
|
| 72 |
+
[37m[1m[2026-09-10 01:44:17,111][13186] Avg episode reward: 32.133, avg true_objective: 13.467[0m
|
| 73 |
+
[36m[2026-09-10 01:44:17,178][13186] Num frames 4100...[0m
|
| 74 |
+
[36m[2026-09-10 01:44:17,282][13186] Num frames 4200...[0m
|
| 75 |
+
[36m[2026-09-10 01:44:17,388][13186] Num frames 4300...[0m
|
| 76 |
+
[36m[2026-09-10 01:44:17,492][13186] Num frames 4400...[0m
|
| 77 |
+
[36m[2026-09-10 01:44:17,600][13186] Num frames 4500...[0m
|
| 78 |
+
[36m[2026-09-10 01:44:17,703][13186] Num frames 4600...[0m
|
| 79 |
+
[36m[2026-09-10 01:44:17,815][13186] Num frames 4700...[0m
|
| 80 |
+
[36m[2026-09-10 01:44:17,919][13186] Num frames 4800...[0m
|
| 81 |
+
[36m[2026-09-10 01:44:18,023][13186] Num frames 4900...[0m
|
| 82 |
+
[37m[1m[2026-09-10 01:44:18,160][13186] Avg episode rewards: #0: 29.447, true rewards: #0: 12.447[0m
|
| 83 |
+
[37m[1m[2026-09-10 01:44:18,160][13186] Avg episode reward: 29.447, avg true_objective: 12.447[0m
|
| 84 |
+
[36m[2026-09-10 01:44:18,183][13186] Num frames 5000...[0m
|
| 85 |
+
[36m[2026-09-10 01:44:18,286][13186] Num frames 5100...[0m
|
| 86 |
+
[36m[2026-09-10 01:44:18,394][13186] Num frames 5200...[0m
|
| 87 |
+
[36m[2026-09-10 01:44:18,498][13186] Num frames 5300...[0m
|
| 88 |
+
[36m[2026-09-10 01:44:18,602][13186] Num frames 5400...[0m
|
| 89 |
+
[36m[2026-09-10 01:44:18,703][13186] Num frames 5500...[0m
|
| 90 |
+
[36m[2026-09-10 01:44:18,816][13186] Num frames 5600...[0m
|
| 91 |
+
[36m[2026-09-10 01:44:18,919][13186] Num frames 5700...[0m
|
| 92 |
+
[36m[2026-09-10 01:44:19,037][13186] Num frames 5800...[0m
|
| 93 |
+
[36m[2026-09-10 01:44:19,193][13186] Num frames 5900...[0m
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+
[36m[2026-09-10 01:44:19,338][13186] Num frames 6000...[0m
|
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+
[36m[2026-09-10 01:44:19,483][13186] Num frames 6100...[0m
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+
[36m[2026-09-10 01:44:19,618][13186] Num frames 6200...[0m
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+
[36m[2026-09-10 01:44:19,748][13186] Num frames 6300...[0m
|
| 98 |
+
[37m[1m[2026-09-10 01:44:19,912][13186] Avg episode rewards: #0: 30.560, true rewards: #0: 12.760[0m
|
| 99 |
+
[37m[1m[2026-09-10 01:44:19,913][13186] Avg episode reward: 30.560, avg true_objective: 12.760[0m
|
| 100 |
+
[36m[2026-09-10 01:44:19,942][13186] Num frames 6400...[0m
|
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+
[36m[2026-09-10 01:44:20,084][13186] Num frames 6500...[0m
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+
[36m[2026-09-10 01:44:20,227][13186] Num frames 6600...[0m
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[36m[2026-09-10 01:44:20,356][13186] Num frames 6700...[0m
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+
[36m[2026-09-10 01:44:20,489][13186] Num frames 6800...[0m
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[36m[2026-09-10 01:44:20,638][13186] Num frames 6900...[0m
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[36m[2026-09-10 01:44:20,772][13186] Num frames 7000...[0m
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[36m[2026-09-10 01:44:21,215][13186] Num frames 7300...[0m
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[36m[2026-09-10 01:44:22,013][13186] Num frames 8000...[0m
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[36m[2026-09-10 01:44:22,116][13186] Num frames 8100...[0m
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[37m[1m[2026-09-10 01:45:02,162][13186] Avg episode rewards: #0: 29.396, true rewards: #0: 11.757[0m
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[37m[1m[2026-09-10 01:45:04,547][13186] Avg episode rewards: #0: 29.065, true rewards: #0: 11.670[0m
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[37m[1m[2026-09-10 01:45:08,190][13186] Avg episode rewards: #0: 29.776, true rewards: #0: 11.901[0m
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[37m[1m[2026-09-10 01:45:20,737][13186] Avg episode rewards: #0: 30.240, true rewards: #0: 11.893[0m
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[37m[1m[2026-09-10 01:45:20,737][13186] Avg episode reward: 30.240, avg true_objective: 11.893[0m
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[37m[1m[2026-09-10 01:45:22,258][13186] Avg episode rewards: #0: 30.159, true rewards: #0: 11.879[0m
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[37m[1m[2026-09-10 01:45:22,259][13186] Avg episode reward: 30.159, avg true_objective: 11.879[0m
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[37m[1m[2026-09-10 01:45:23,030][13186] Avg episode rewards: #0: 29.766, true rewards: #0: 11.747[0m
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[37m[1m[2026-09-10 01:45:24,198][13186] Avg episode rewards: #0: 29.565, true rewards: #0: 11.699[0m
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[37m[1m[2026-09-10 01:45:25,103][13186] Avg episode rewards: #0: 29.384, true rewards: #0: 11.630[0m
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[37m[1m[2026-09-10 01:45:26,646][13186] Avg episode rewards: #0: 29.435, true rewards: #0: 11.657[0m
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[37m[1m[2026-09-10 01:45:27,770][13186] Avg episode rewards: #0: 29.311, true rewards: #0: 11.620[0m
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[37m[1m[2026-09-10 01:45:28,413][13186] Avg episode rewards: #0: 28.970, true rewards: #0: 11.506[0m
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[37m[1m[2026-09-10 01:45:30,277][13186] Avg episode rewards: #0: 29.169, true rewards: #0: 11.590[0m
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| 800 |
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[37m[1m[2026-09-10 01:45:30,277][13186] Avg episode reward: 29.169, avg true_objective: 11.590[0m
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[37m[1m[2026-09-10 01:45:31,975][13186] Avg episode rewards: #0: 29.367, true rewards: #0: 11.661[0m
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[37m[1m[2026-09-10 01:45:32,660][13186] Avg episode rewards: #0: 29.057, true rewards: #0: 11.566[0m
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[37m[1m[2026-09-10 01:45:32,660][13186] Avg episode reward: 29.057, avg true_objective: 11.566[0m
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[37m[1m[2026-09-10 01:45:33,436][13186] Avg episode rewards: #0: 28.824, true rewards: #0: 11.491[0m
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[37m[1m[2026-09-10 01:45:34,260][13186] Avg episode rewards: #0: 28.555, true rewards: #0: 11.391[0m
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[37m[1m[2026-09-10 01:45:34,261][13186] Avg episode reward: 28.555, avg true_objective: 11.391[0m
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[37m[1m[2026-09-10 01:45:36,027][13186] Avg episode rewards: #0: 28.594, true rewards: #0: 11.401[0m
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[37m[1m[2026-09-10 01:45:36,027][13186] Avg episode reward: 28.594, avg true_objective: 11.401[0m
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[37m[1m[2026-09-10 01:45:37,668][13186] Avg episode rewards: #0: 28.760, true rewards: #0: 11.459[0m
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[37m[1m[2026-09-10 01:45:37,669][13186] Avg episode reward: 28.760, avg true_objective: 11.459[0m
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[37m[1m[2026-09-10 01:45:38,554][13186] Avg episode rewards: #0: 28.545, true rewards: #0: 11.405[0m
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[37m[1m[2026-09-10 01:45:38,554][13186] Avg episode reward: 28.545, avg true_objective: 11.405[0m
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[37m[1m[2026-09-10 01:45:40,387][13186] Avg episode rewards: #0: 28.859, true rewards: #0: 11.490[0m
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[37m[1m[2026-09-10 01:45:40,387][13186] Avg episode reward: 28.859, avg true_objective: 11.490[0m
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[37m[1m[2026-09-10 01:45:41,279][13186] Avg episode rewards: #0: 28.660, true rewards: #0: 11.433[0m
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[37m[1m[2026-09-10 01:45:41,279][13186] Avg episode reward: 28.660, avg true_objective: 11.433[0m
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[37m[1m[2026-09-10 01:45:41,824][13186] Avg episode rewards: #0: 28.388, true rewards: #0: 11.329[0m
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[37m[1m[2026-09-10 01:45:41,824][13186] Avg episode reward: 28.388, avg true_objective: 11.329[0m
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[36m[2026-09-10 01:45:43,480][13186] Num frames 77400...[0m
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[37m[1m[2026-09-10 01:45:44,191][13186] Avg episode rewards: #0: 28.824, true rewards: #0: 11.471[0m
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[37m[1m[2026-09-10 01:45:44,192][13186] Avg episode reward: 28.824, avg true_objective: 11.471[0m
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[37m[1m[2026-09-10 01:45:46,183][13186] Avg episode rewards: #0: 29.115, true rewards: #0: 11.564[0m
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[37m[1m[2026-09-10 01:45:46,183][13186] Avg episode reward: 29.115, avg true_objective: 11.564[0m
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[37m[1m[2026-09-10 01:45:48,511][13186] Avg episode rewards: #0: 29.296, true rewards: #0: 11.639[0m
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[37m[1m[2026-09-10 01:45:48,511][13186] Avg episode reward: 29.296, avg true_objective: 11.639[0m
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[36m[2026-09-10 01:45:48,542][13186] Num frames 81500...[0m
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[37m[1m[2026-09-10 01:45:49,270][13186] Avg episode rewards: #0: 29.116, true rewards: #0: 11.567[0m
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| 989 |
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[37m[1m[2026-09-10 01:45:49,270][13186] Avg episode reward: 29.116, avg true_objective: 11.567[0m
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[36m[2026-09-10 01:45:49,349][13186] Num frames 82200...[0m
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[37m[1m[2026-09-10 01:45:49,689][13186] Avg episode rewards: #0: 28.801, true rewards: #0: 11.454[0m
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[37m[1m[2026-09-10 01:45:49,689][13186] Avg episode reward: 28.801, avg true_objective: 11.454[0m
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[36m[2026-09-10 01:45:50,261][13186] Num frames 83000...[0m
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[37m[1m[2026-09-10 01:45:51,051][13186] Avg episode rewards: #0: 28.862, true rewards: #0: 11.465[0m
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[37m[1m[2026-09-10 01:45:51,051][13186] Avg episode reward: 28.862, avg true_objective: 11.465[0m
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[37m[1m[2026-09-10 01:45:51,840][13186] Avg episode rewards: #0: 28.698, true rewards: #0: 11.400[0m
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[37m[1m[2026-09-10 01:45:51,840][13186] Avg episode reward: 28.698, avg true_objective: 11.400[0m
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[37m[1m[2026-09-10 01:45:52,922][13186] Avg episode rewards: #0: 28.576, true rewards: #0: 11.376[0m
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[37m[1m[2026-09-10 01:45:52,922][13186] Avg episode reward: 28.576, avg true_objective: 11.376[0m
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[37m[1m[2026-09-10 01:45:54,362][13186] Avg episode rewards: #0: 28.609, true rewards: #0: 11.398[0m
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[37m[1m[2026-09-10 01:45:54,362][13186] Avg episode reward: 28.609, avg true_objective: 11.398[0m
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[37m[1m[2026-09-10 01:45:55,506][13186] Avg episode rewards: #0: 28.582, true rewards: #0: 11.387[0m
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[37m[1m[2026-09-10 01:45:55,507][13186] Avg episode reward: 28.582, avg true_objective: 11.387[0m
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[37m[1m[2026-09-10 01:45:56,286][13186] Avg episode rewards: #0: 28.421, true rewards: #0: 11.332[0m
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[37m[1m[2026-09-10 01:45:56,286][13186] Avg episode reward: 28.421, avg true_objective: 11.332[0m
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[37m[1m[2026-09-10 01:45:58,638][13186] Avg episode rewards: #0: 28.745, true rewards: #0: 11.454[0m
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[37m[1m[2026-09-10 01:45:58,638][13186] Avg episode reward: 28.745, avg true_objective: 11.454[0m
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[37m[1m[2026-09-10 01:46:01,116][13186] Avg episode rewards: #0: 29.029, true rewards: #0: 11.529[0m
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[37m[1m[2026-09-10 01:46:03,415][13186] Avg episode rewards: #0: 29.374, true rewards: #0: 11.646[0m
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[37m[1m[2026-09-10 01:46:05,196][13186] Avg episode rewards: #0: 29.521, true rewards: #0: 11.704[0m
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[37m[1m[2026-09-10 01:46:06,627][13186] Avg episode rewards: #0: 29.632, true rewards: #0: 11.717[0m
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[37m[1m[2026-09-10 01:46:08,595][13186] Avg episode rewards: #0: 29.795, true rewards: #0: 11.783[0m
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[37m[1m[2026-09-10 01:46:11,661][13186] Avg episode rewards: #0: 29.857, true rewards: #0: 11.810[0m
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[37m[1m[2026-09-10 01:46:13,377][13186] Avg episode rewards: #0: 29.808, true rewards: #0: 11.808[0m
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[37m[1m[2026-09-10 01:46:13,378][13186] Avg episode reward: 29.808, avg true_objective: 11.808[0m
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[37m[1m[2026-09-10 01:46:14,920][13186] Avg episode rewards: #0: 29.871, true rewards: #0: 11.826[0m
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[37m[1m[2026-09-10 01:46:14,920][13186] Avg episode reward: 29.871, avg true_objective: 11.826[0m
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[37m[1m[2026-09-10 01:46:16,391][13186] Avg episode rewards: #0: 29.988, true rewards: #0: 11.842[0m
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[37m[1m[2026-09-10 01:46:16,391][13186] Avg episode reward: 29.988, avg true_objective: 11.842[0m
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[37m[1m[2026-09-10 01:46:18,414][13186] Avg episode rewards: #0: 30.220, true rewards: #0: 11.920[0m
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[37m[1m[2026-09-10 01:46:18,415][13186] Avg episode reward: 30.220, avg true_objective: 11.920[0m
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[37m[1m[2026-09-10 01:46:20,680][13186] Avg episode rewards: #0: 30.580, true rewards: #0: 12.020[0m
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[37m[1m[2026-09-10 01:46:20,681][13186] Avg episode reward: 30.580, avg true_objective: 12.020[0m
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[36m[2026-09-10 01:46:20,703][13186] Num frames 109400...[0m
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[37m[1m[2026-09-10 01:46:21,137][13186] Avg episode rewards: #0: 30.333, true rewards: #0: 11.931[0m
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| 1307 |
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[37m[1m[2026-09-10 01:46:21,137][13186] Avg episode reward: 30.333, avg true_objective: 11.931[0m
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[37m[1m[2026-09-10 01:46:22,696][13186] Avg episode rewards: #0: 30.395, true rewards: #0: 11.955[0m
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[37m[1m[2026-09-10 01:46:22,696][13186] Avg episode reward: 30.395, avg true_objective: 11.955[0m
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[37m[1m[2026-09-10 01:46:23,145][13186] Avg episode rewards: #0: 30.176, true rewards: #0: 11.867[0m
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| 1329 |
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[37m[1m[2026-09-10 01:46:23,145][13186] Avg episode reward: 30.176, avg true_objective: 11.867[0m
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[36m[2026-09-10 01:46:23,195][13186] Num frames 111600...[0m
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[37m[1m[2026-09-10 01:46:24,948][13186] Avg episode rewards: #0: 30.168, true rewards: #0: 11.874[0m
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[37m[1m[2026-09-10 01:46:24,949][13186] Avg episode reward: 30.168, avg true_objective: 11.874[0m
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[37m[1m[2026-09-10 01:46:25,830][13186] Avg episode rewards: #0: 29.959, true rewards: #0: 11.814[0m
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[37m[1m[2026-09-10 01:46:26,385][13186] Avg episode rewards: #0: 29.762, true rewards: #0: 11.741[0m
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| 1370 |
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[36m[2026-09-10 01:46:27,551][13186] Num frames 115000...[0m
|
| 1371 |
+
[37m[1m[2026-09-10 01:46:27,650][13186] Avg episode rewards: #0: 29.698, true rewards: #0: 11.739[0m
|
| 1372 |
+
[37m[1m[2026-09-10 01:46:27,651][13186] Avg episode reward: 29.698, avg true_objective: 11.739[0m
|
| 1373 |
+
[36m[2026-09-10 01:46:27,709][13186] Num frames 115100...[0m
|
| 1374 |
+
[36m[2026-09-10 01:46:27,812][13186] Num frames 115200...[0m
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| 1375 |
+
[36m[2026-09-10 01:46:27,918][13186] Num frames 115300...[0m
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| 1376 |
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[36m[2026-09-10 01:46:28,024][13186] Num frames 115400...[0m
|
| 1377 |
+
[36m[2026-09-10 01:46:28,133][13186] Num frames 115500...[0m
|
| 1378 |
+
[36m[2026-09-10 01:46:28,244][13186] Num frames 115600...[0m
|
| 1379 |
+
[36m[2026-09-10 01:46:28,350][13186] Num frames 115700...[0m
|
| 1380 |
+
[36m[2026-09-10 01:46:28,454][13186] Num frames 115800...[0m
|
| 1381 |
+
[36m[2026-09-10 01:46:28,559][13186] Num frames 115900...[0m
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| 1382 |
+
[36m[2026-09-10 01:46:28,672][13186] Num frames 116000...[0m
|
| 1383 |
+
[36m[2026-09-10 01:46:28,777][13186] Num frames 116100...[0m
|
| 1384 |
+
[36m[2026-09-10 01:46:28,884][13186] Num frames 116200...[0m
|
| 1385 |
+
[36m[2026-09-10 01:46:28,993][13186] Num frames 116300...[0m
|
| 1386 |
+
[36m[2026-09-10 01:46:29,101][13186] Num frames 116400...[0m
|
| 1387 |
+
[36m[2026-09-10 01:46:29,213][13186] Num frames 116500...[0m
|
| 1388 |
+
[36m[2026-09-10 01:46:29,317][13186] Num frames 116600...[0m
|
| 1389 |
+
[36m[2026-09-10 01:46:29,420][13186] Num frames 116700...[0m
|
| 1390 |
+
[36m[2026-09-10 01:46:29,529][13186] Num frames 116800...[0m
|
| 1391 |
+
[36m[2026-09-10 01:46:29,648][13186] Num frames 116900...[0m
|
| 1392 |
+
[36m[2026-09-10 01:46:29,754][13186] Num frames 117000...[0m
|
| 1393 |
+
[36m[2026-09-10 01:46:29,860][13186] Num frames 117100...[0m
|
| 1394 |
+
[37m[1m[2026-09-10 01:46:29,959][13186] Avg episode rewards: #0: 30.004, true rewards: #0: 11.833[0m
|
| 1395 |
+
[37m[1m[2026-09-10 01:46:29,959][13186] Avg episode reward: 30.004, avg true_objective: 11.833[0m
|
| 1396 |
+
[36m[2026-09-10 01:46:30,019][13186] Num frames 117200...[0m
|
| 1397 |
+
[36m[2026-09-10 01:46:30,122][13186] Num frames 117300...[0m
|
| 1398 |
+
[36m[2026-09-10 01:46:30,229][13186] Num frames 117400...[0m
|
| 1399 |
+
[36m[2026-09-10 01:46:30,332][13186] Num frames 117500...[0m
|
| 1400 |
+
[36m[2026-09-10 01:46:30,435][13186] Num frames 117600...[0m
|
| 1401 |
+
[36m[2026-09-10 01:46:30,538][13186] Num frames 117700...[0m
|
| 1402 |
+
[36m[2026-09-10 01:46:30,651][13186] Num frames 117800...[0m
|
| 1403 |
+
[37m[1m[2026-09-10 01:46:30,792][13186] Avg episode rewards: #0: 29.848, true rewards: #0: 11.788[0m
|
| 1404 |
+
[37m[1m[2026-09-10 01:46:30,792][13186] Avg episode reward: 29.848, avg true_objective: 11.788[0m
|
| 1405 |
+
FINAL_EVALUATION {'environment': 'doom_health_gathering_supreme', 'algorithm': 'APPO', 'evaluation_seed': 100001, 'evaluation_episodes': 100, 'mean_reward': 11.787799999999942, 'std_reward': 5.578349501420757, 'checkpoint_files': ['/content/doom-results/Kay-Doom/checkpoint_p0/checkpoint_000000936_3833856.pth', '/content/doom-results/Kay-Doom/checkpoint_p0/checkpoint_000000978_4005888.pth'], 'policy': 'deterministic'}
|
| 1406 |
+
(0, 29.847589435577394)
|
doom-training.log
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
evaluate_doom.py
ADDED
|
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import inspect,pathlib
|
| 2 |
+
import sample_factory.enjoy as module
|
| 3 |
+
source=inspect.getsource(module)
|
| 4 |
+
source=source.replace("obs, infos = env.reset()", "env.unwrapped.seed(100001)"+chr(10)+" obs, infos = env.reset()")
|
| 5 |
+
report_code=""" import json
|
| 6 |
+
from pathlib import Path
|
| 7 |
+
report={'environment':cfg.env,'algorithm':'APPO','evaluation_seed':100001,'evaluation_episodes':len(reward_list),'mean_reward':float(np.mean(reward_list)),'std_reward':float(np.std(reward_list)),'rewards':[float(x) for x in reward_list],'checkpoint_files':[str(x) for x in checkpoints],'policy':'deterministic'}
|
| 8 |
+
Path('/content/doom-results/Kay-Doom/evaluation.json').write_text(json.dumps(report,indent=2))
|
| 9 |
+
print('FINAL_EVALUATION', {k:v for k,v in report.items() if k!='rewards'},flush=True)
|
| 10 |
+
"""
|
| 11 |
+
source=source.replace(' return ExperimentStatus.SUCCESS,',report_code+' return ExperimentStatus.SUCCESS,')
|
| 12 |
+
namespace={};exec(source,namespace)
|
| 13 |
+
from train_doom import register_vizdoom_components,parse_vizdoom_cfg
|
| 14 |
+
register_vizdoom_components()
|
| 15 |
+
cfg=parse_vizdoom_cfg(argv=['--env=doom_health_gathering_supreme','--experiment=Kay-Doom','--train_dir=/content/doom-results','--no_render','--max_num_episodes=100','--eval_deterministic=True','--load_checkpoint_kind=latest'],evaluation=True)
|
| 16 |
+
print(namespace['enjoy'](cfg))
|
evaluation.json
ADDED
|
@@ -0,0 +1,117 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"environment": "doom_health_gathering_supreme",
|
| 3 |
+
"algorithm": "APPO",
|
| 4 |
+
"evaluation_seed": 100001,
|
| 5 |
+
"evaluation_episodes": 100,
|
| 6 |
+
"mean_reward": 11.787799999999942,
|
| 7 |
+
"std_reward": 5.578349501420757,
|
| 8 |
+
"rewards": [
|
| 9 |
+
19.07000000000018,
|
| 10 |
+
10.55999999999982,
|
| 11 |
+
10.769999999999815,
|
| 12 |
+
9.389999999999844,
|
| 13 |
+
14.009999999999746,
|
| 14 |
+
21.000000000000483,
|
| 15 |
+
20.170000000000353,
|
| 16 |
+
4.689999999999944,
|
| 17 |
+
17.2799999999999,
|
| 18 |
+
8.959999999999853,
|
| 19 |
+
12.239999999999783,
|
| 20 |
+
21.000000000000483,
|
| 21 |
+
16.75999999999982,
|
| 22 |
+
3.519999999999969,
|
| 23 |
+
21.000000000000483,
|
| 24 |
+
5.759999999999922,
|
| 25 |
+
13.679999999999753,
|
| 26 |
+
10.55999999999982,
|
| 27 |
+
7.3599999999998875,
|
| 28 |
+
16.6399999999998,
|
| 29 |
+
12.819999999999771,
|
| 30 |
+
10.239999999999826,
|
| 31 |
+
3.1999999999999758,
|
| 32 |
+
9.919999999999833,
|
| 33 |
+
5.119999999999935,
|
| 34 |
+
8.319999999999867,
|
| 35 |
+
13.759999999999751,
|
| 36 |
+
5.089999999999936,
|
| 37 |
+
5.4399999999999284,
|
| 38 |
+
12.479999999999778,
|
| 39 |
+
3.839999999999962,
|
| 40 |
+
14.399999999999737,
|
| 41 |
+
21.000000000000483,
|
| 42 |
+
16.579999999999792,
|
| 43 |
+
10.879999999999812,
|
| 44 |
+
5.759999999999922,
|
| 45 |
+
5.789999999999921,
|
| 46 |
+
14.399999999999737,
|
| 47 |
+
21.000000000000483,
|
| 48 |
+
11.569999999999798,
|
| 49 |
+
3.7299999999999645,
|
| 50 |
+
4.009999999999959,
|
| 51 |
+
8.21999999999987,
|
| 52 |
+
2.389999999999993,
|
| 53 |
+
18.5500000000001,
|
| 54 |
+
10.879999999999812,
|
| 55 |
+
21.000000000000483,
|
| 56 |
+
21.000000000000483,
|
| 57 |
+
16.95999999999985,
|
| 58 |
+
11.199999999999806,
|
| 59 |
+
5.119999999999935,
|
| 60 |
+
9.279999999999847,
|
| 61 |
+
7.999999999999874,
|
| 62 |
+
13.119999999999765,
|
| 63 |
+
9.59999999999984,
|
| 64 |
+
5.229999999999933,
|
| 65 |
+
16.31999999999975,
|
| 66 |
+
15.67999999999971,
|
| 67 |
+
6.079999999999915,
|
| 68 |
+
7.039999999999894,
|
| 69 |
+
5.4399999999999284,
|
| 70 |
+
11.96999999999979,
|
| 71 |
+
15.039999999999724,
|
| 72 |
+
7.999999999999874,
|
| 73 |
+
16.979999999999855,
|
| 74 |
+
7.679999999999881,
|
| 75 |
+
4.479999999999949,
|
| 76 |
+
21.000000000000483,
|
| 77 |
+
17.92,
|
| 78 |
+
16.74999999999982,
|
| 79 |
+
6.579999999999904,
|
| 80 |
+
3.4099999999999713,
|
| 81 |
+
12.249999999999783,
|
| 82 |
+
6.689999999999902,
|
| 83 |
+
9.59999999999984,
|
| 84 |
+
13.039999999999766,
|
| 85 |
+
10.55999999999982,
|
| 86 |
+
7.039999999999894,
|
| 87 |
+
21.000000000000483,
|
| 88 |
+
17.44999999999993,
|
| 89 |
+
21.000000000000483,
|
| 90 |
+
16.37999999999976,
|
| 91 |
+
12.799999999999772,
|
| 92 |
+
17.2799999999999,
|
| 93 |
+
16.95999999999985,
|
| 94 |
+
8.959999999999853,
|
| 95 |
+
11.589999999999797,
|
| 96 |
+
13.409999999999759,
|
| 97 |
+
13.279999999999761,
|
| 98 |
+
18.81000000000014,
|
| 99 |
+
21.000000000000483,
|
| 100 |
+
3.899999999999961,
|
| 101 |
+
14.079999999999744,
|
| 102 |
+
3.7699999999999636,
|
| 103 |
+
12.479999999999778,
|
| 104 |
+
6.079999999999915,
|
| 105 |
+
4.799999999999942,
|
| 106 |
+
11.519999999999799,
|
| 107 |
+
21.000000000000483,
|
| 108 |
+
7.3599999999998875
|
| 109 |
+
],
|
| 110 |
+
"checkpoint_files": [
|
| 111 |
+
"/content/doom-results/Kay-Doom/checkpoint_p0/checkpoint_000000936_3833856.pth",
|
| 112 |
+
"/content/doom-results/Kay-Doom/checkpoint_p0/checkpoint_000000978_4005888.pth"
|
| 113 |
+
],
|
| 114 |
+
"policy": "deterministic",
|
| 115 |
+
"selected_checkpoint": "checkpoint_p0/checkpoint_000000978_4005888.pth",
|
| 116 |
+
"reward_definition": "Sample Factory true_objective episode return, matching its official model-card reward_list"
|
| 117 |
+
}
|
train_doom.py
ADDED
|
@@ -0,0 +1,42 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import functools
|
| 2 |
+
|
| 3 |
+
from sample_factory.algo.utils.context import global_model_factory
|
| 4 |
+
from sample_factory.cfg.arguments import parse_full_cfg, parse_sf_args
|
| 5 |
+
from sample_factory.envs.env_utils import register_env
|
| 6 |
+
from sample_factory.train import run_rl
|
| 7 |
+
|
| 8 |
+
from sf_examples.vizdoom.doom.doom_model import make_vizdoom_encoder
|
| 9 |
+
from sf_examples.vizdoom.doom.doom_params import add_doom_env_args, doom_override_defaults
|
| 10 |
+
from sf_examples.vizdoom.doom.doom_utils import DOOM_ENVS, make_doom_env_from_spec
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
# Registers all the ViZDoom environments
|
| 14 |
+
def register_vizdoom_envs():
|
| 15 |
+
for env_spec in DOOM_ENVS:
|
| 16 |
+
make_env_func = functools.partial(make_doom_env_from_spec, env_spec)
|
| 17 |
+
register_env(env_spec.name, make_env_func)
|
| 18 |
+
|
| 19 |
+
# Sample Factory allows the registration of a custom Neural Network architecture
|
| 20 |
+
# See https://github.com/alex-petrenko/sample-factory/blob/master/sf_examples/vizdoom/doom/doom_model.py for more details
|
| 21 |
+
def register_vizdoom_models():
|
| 22 |
+
global_model_factory().register_encoder_factory(make_vizdoom_encoder)
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
def register_vizdoom_components():
|
| 26 |
+
register_vizdoom_envs()
|
| 27 |
+
register_vizdoom_models()
|
| 28 |
+
|
| 29 |
+
# parse the command line args and create a config
|
| 30 |
+
def parse_vizdoom_cfg(argv=None, evaluation=False):
|
| 31 |
+
parser, _ = parse_sf_args(argv=argv, evaluation=evaluation)
|
| 32 |
+
# parameters specific to Doom envs
|
| 33 |
+
add_doom_env_args(parser)
|
| 34 |
+
# override Doom default values for algo parameters
|
| 35 |
+
doom_override_defaults(parser)
|
| 36 |
+
# second parsing pass yields the final configuration
|
| 37 |
+
final_cfg = parse_full_cfg(parser, argv)
|
| 38 |
+
return final_cfg
|
| 39 |
+
if __name__ == "__main__":
|
| 40 |
+
register_vizdoom_components()
|
| 41 |
+
cfg=parse_vizdoom_cfg(argv=["--env=doom_health_gathering_supreme","--num_workers=8","--num_envs_per_worker=4","--train_for_env_steps=4000000","--experiment=Kay-Doom","--train_dir=/content/doom-results","--seed=42","--save_every_sec=60"])
|
| 42 |
+
raise SystemExit(run_rl(cfg))
|