Upload folder using huggingface_hub
Browse files- .gitattributes +5 -0
- .summary/0/events.out.tfevents.1714107045.41662289211e +3 -0
- .summary/0/events.out.tfevents.1714108128.41662289211e +3 -0
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- .summary/0/events.out.tfevents.1714324047.41662289211e +3 -0
- README.md +42 -0
- a100_100x100.mp4 +3 -0
- a20_40x40.mp4 +3 -0
- a5_25x25.mp4 +3 -0
- checkpoint_p0/best_000422462_6921617408_reward_0.757.pth +3 -0
- checkpoint_p0/checkpoint_000701974_11501142016.pth +3 -0
- checkpoint_p0/checkpoint_000702383_11507843072.pth +3 -0
- config.json +158 -0
- git.diff +0 -0
- replay.mp4 +3 -0
- sf_log.txt +3 -0
.gitattributes
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README.md
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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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---
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| 8 |
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| 9 |
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A(n) **APPO** model trained on the **GDY-PowerGrid** environment.
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| 10 |
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| 11 |
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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/
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| 13 |
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## Downloading the model
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| 16 |
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| 17 |
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After installing Sample-Factory, download the model with:
|
| 18 |
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```
|
| 19 |
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python -m sample_factory.huggingface.load_from_hub -r metta-ai/baseline.v0.1.0
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| 20 |
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```
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| 21 |
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| 22 |
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## Using the model
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| 24 |
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To run the model after download, use the `enjoy` script corresponding to this environment:
|
| 26 |
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```
|
| 27 |
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python -m <path.to.enjoy.module> --algo=APPO --env=GDY-PowerGrid --train_dir=./train_dir --experiment=baseline.v0.1.0
|
| 28 |
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```
|
| 29 |
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|
| 30 |
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| 31 |
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You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
|
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See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
|
| 33 |
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| 34 |
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## Training with this model
|
| 35 |
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|
| 36 |
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To continue training with this model, use the `train` script corresponding to this environment:
|
| 37 |
+
```
|
| 38 |
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python -m <path.to.train.module> --algo=APPO --env=GDY-PowerGrid --train_dir=./train_dir --experiment=baseline.v0.1.0 --restart_behavior=resume --train_for_env_steps=10000000000
|
| 39 |
+
```
|
| 40 |
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| 41 |
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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.
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| 42 |
+
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a100_100x100.mp4
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a20_40x40.mp4
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a5_25x25.mp4
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checkpoint_p0/best_000422462_6921617408_reward_0.757.pth
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checkpoint_p0/checkpoint_000701974_11501142016.pth
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config.json
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|
| 1 |
+
{
|
| 2 |
+
"help": false,
|
| 3 |
+
"algo": "APPO",
|
| 4 |
+
"env": "GDY-PowerGrid",
|
| 5 |
+
"experiment": "p2.fa.clean",
|
| 6 |
+
"train_dir": "/workspace/metta/train_dir",
|
| 7 |
+
"restart_behavior": "resume",
|
| 8 |
+
"device": "gpu",
|
| 9 |
+
"seed": null,
|
| 10 |
+
"num_policies": 1,
|
| 11 |
+
"async_rl": true,
|
| 12 |
+
"serial_mode": false,
|
| 13 |
+
"batched_sampling": false,
|
| 14 |
+
"num_batches_to_accumulate": 2,
|
| 15 |
+
"worker_num_splits": 2,
|
| 16 |
+
"policy_workers_per_policy": 1,
|
| 17 |
+
"max_policy_lag": 50,
|
| 18 |
+
"num_workers": 32,
|
| 19 |
+
"num_envs_per_worker": 2,
|
| 20 |
+
"batch_size": 16384,
|
| 21 |
+
"num_batches_per_epoch": 1,
|
| 22 |
+
"num_epochs": 1,
|
| 23 |
+
"rollout": 256,
|
| 24 |
+
"recurrence": 256,
|
| 25 |
+
"shuffle_minibatches": false,
|
| 26 |
+
"gamma": 0.99,
|
| 27 |
+
"reward_scale": 1.0,
|
| 28 |
+
"reward_clip": 1000.0,
|
| 29 |
+
"value_bootstrap": false,
|
| 30 |
+
"normalize_returns": true,
|
| 31 |
+
"exploration_loss_coeff": 0.002,
|
| 32 |
+
"value_loss_coeff": 0.976,
|
| 33 |
+
"kl_loss_coeff": 0.0,
|
| 34 |
+
"aux_loss_coeff": 0.0,
|
| 35 |
+
"exploration_loss": "symmetric_kl",
|
| 36 |
+
"gae_lambda": 0.95,
|
| 37 |
+
"ppo_clip_ratio": 0.1,
|
| 38 |
+
"ppo_clip_value": 1.0,
|
| 39 |
+
"with_vtrace": false,
|
| 40 |
+
"vtrace_rho": 1.0,
|
| 41 |
+
"vtrace_c": 1.0,
|
| 42 |
+
"optimizer": "adam",
|
| 43 |
+
"adam_eps": 1e-06,
|
| 44 |
+
"adam_beta1": 0.9,
|
| 45 |
+
"adam_beta2": 0.999,
|
| 46 |
+
"max_grad_norm": 4.0,
|
| 47 |
+
"learning_rate": 0.0001,
|
| 48 |
+
"lr_schedule": "constant",
|
| 49 |
+
"lr_schedule_kl_threshold": 0.008,
|
| 50 |
+
"lr_adaptive_min": 1e-06,
|
| 51 |
+
"lr_adaptive_max": 0.01,
|
| 52 |
+
"obs_subtract_mean": 0.0,
|
| 53 |
+
"obs_scale": 1.0,
|
| 54 |
+
"normalize_input": false,
|
| 55 |
+
"normalize_input_keys": null,
|
| 56 |
+
"decorrelate_experience_max_seconds": 150,
|
| 57 |
+
"decorrelate_envs_on_one_worker": true,
|
| 58 |
+
"actor_worker_gpus": [],
|
| 59 |
+
"set_workers_cpu_affinity": true,
|
| 60 |
+
"force_envs_single_thread": false,
|
| 61 |
+
"default_niceness": 0,
|
| 62 |
+
"log_to_file": true,
|
| 63 |
+
"experiment_summaries_interval": 10,
|
| 64 |
+
"flush_summaries_interval": 30,
|
| 65 |
+
"stats_avg": 100,
|
| 66 |
+
"summaries_use_frameskip": true,
|
| 67 |
+
"heartbeat_interval": 20,
|
| 68 |
+
"heartbeat_reporting_interval": 180,
|
| 69 |
+
"train_for_env_steps": 9999999999999,
|
| 70 |
+
"train_for_seconds": 10000000000,
|
| 71 |
+
"save_every_sec": 120,
|
| 72 |
+
"keep_checkpoints": 2,
|
| 73 |
+
"load_checkpoint_path": null,
|
| 74 |
+
"init_checkpoint_path": null,
|
| 75 |
+
"load_checkpoint_kind": "latest",
|
| 76 |
+
"save_milestones_sec": -1,
|
| 77 |
+
"save_best_every_sec": 5,
|
| 78 |
+
"save_best_metric": "reward",
|
| 79 |
+
"save_best_after": 100000,
|
| 80 |
+
"benchmark": false,
|
| 81 |
+
"encoder_mlp_layers": [
|
| 82 |
+
512,
|
| 83 |
+
512
|
| 84 |
+
],
|
| 85 |
+
"encoder_conv_architecture": "convnet_simple",
|
| 86 |
+
"encoder_conv_mlp_layers": [
|
| 87 |
+
512
|
| 88 |
+
],
|
| 89 |
+
"use_rnn": true,
|
| 90 |
+
"rnn_size": 512,
|
| 91 |
+
"rnn_type": "gru",
|
| 92 |
+
"rnn_num_layers": 1,
|
| 93 |
+
"decoder_mlp_layers": [],
|
| 94 |
+
"nonlinearity": "elu",
|
| 95 |
+
"policy_initialization": "orthogonal",
|
| 96 |
+
"policy_init_gain": 1.0,
|
| 97 |
+
"actor_critic_share_weights": true,
|
| 98 |
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"adaptive_stddev": true,
|
| 99 |
+
"continuous_tanh_scale": 0.0,
|
| 100 |
+
"initial_stddev": 1.0,
|
| 101 |
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"use_env_info_cache": false,
|
| 102 |
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"env_gpu_actions": false,
|
| 103 |
+
"env_gpu_observations": true,
|
| 104 |
+
"env_frameskip": 1,
|
| 105 |
+
"env_framestack": 1,
|
| 106 |
+
"pixel_format": "CHW",
|
| 107 |
+
"use_record_episode_statistics": false,
|
| 108 |
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"episode_counter": false,
|
| 109 |
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"with_wandb": true,
|
| 110 |
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"wandb_user": "platypus",
|
| 111 |
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"wandb_project": "sample_factory",
|
| 112 |
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"wandb_group": null,
|
| 113 |
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"wandb_job_type": "SF",
|
| 114 |
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"wandb_tags": [],
|
| 115 |
+
"with_pbt": false,
|
| 116 |
+
"pbt_mix_policies_in_one_env": true,
|
| 117 |
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"pbt_period_env_steps": 5000000,
|
| 118 |
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"pbt_start_mutation": 20000000,
|
| 119 |
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"pbt_replace_fraction": 0.3,
|
| 120 |
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"pbt_mutation_rate": 0.15,
|
| 121 |
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"pbt_replace_reward_gap": 0.1,
|
| 122 |
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"pbt_replace_reward_gap_absolute": 1e-06,
|
| 123 |
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"pbt_optimize_gamma": false,
|
| 124 |
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"pbt_target_objective": "true_objective",
|
| 125 |
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"pbt_perturb_min": 1.1,
|
| 126 |
+
"pbt_perturb_max": 1.5,
|
| 127 |
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"env_cfg": "{\"wall_density\": 0.01, \"altar:cost\": 100, \"cost:shield\": 0, \"num_agents\": 20, \"width\": 40, \"height\": 40, \"num_altars\": 5, \"num_chargers\": 10, \"num_generators\": 50}",
|
| 128 |
+
"command_line": "--aux_loss_coef=0 --recurrence=256 --rollout=256 --value_loss_coeff=0.976 --exploration_loss=symmetric_kl --exploration_loss_coeff=0.002 --policy_initialization=orthogonal --learning_rate=0.0001 --max_policy_lag=50 --nonlinearity=elu --load_checkpoint_kind=latest --normalize_input=False --batch_size=16384 --decorrelate_experience_max_seconds=150 --train_for_env_steps=9999999999999 --with_wandb=True --wandb_user=platypus --wandb_project=sample_factory --experiment=p2.fa.clean --rnn_num_layers=1 --rnn_size=512 --rnn_type=gru --env=GDY-PowerGrid --env_cfg={\"wall_density\": 0.01, \"altar:cost\": 100, \"cost:shield\": 0, \"num_agents\": 20, \"width\": 40, \"height\": 40, \"num_altars\": 5, \"num_chargers\": 10, \"num_generators\": 50}",
|
| 129 |
+
"cli_args": {
|
| 130 |
+
"env": "GDY-PowerGrid",
|
| 131 |
+
"experiment": "p2.fa.clean",
|
| 132 |
+
"max_policy_lag": 50,
|
| 133 |
+
"batch_size": 16384,
|
| 134 |
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"rollout": 256,
|
| 135 |
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"recurrence": 256,
|
| 136 |
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"exploration_loss_coeff": 0.002,
|
| 137 |
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"value_loss_coeff": 0.976,
|
| 138 |
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"aux_loss_coeff": 0.0,
|
| 139 |
+
"exploration_loss": "symmetric_kl",
|
| 140 |
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"learning_rate": 0.0001,
|
| 141 |
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"normalize_input": false,
|
| 142 |
+
"decorrelate_experience_max_seconds": 150,
|
| 143 |
+
"train_for_env_steps": 9999999999999,
|
| 144 |
+
"load_checkpoint_kind": "latest",
|
| 145 |
+
"rnn_size": 512,
|
| 146 |
+
"rnn_type": "gru",
|
| 147 |
+
"rnn_num_layers": 1,
|
| 148 |
+
"nonlinearity": "elu",
|
| 149 |
+
"policy_initialization": "orthogonal",
|
| 150 |
+
"with_wandb": true,
|
| 151 |
+
"wandb_user": "platypus",
|
| 152 |
+
"wandb_project": "sample_factory",
|
| 153 |
+
"env_cfg": "{\"wall_density\": 0.01, \"altar:cost\": 100, \"cost:shield\": 0, \"num_agents\": 20, \"width\": 40, \"height\": 40, \"num_altars\": 5, \"num_chargers\": 10, \"num_generators\": 50}"
|
| 154 |
+
},
|
| 155 |
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"git_hash": "c1f8312bb9edc5b2b815fa011904740d7104e699",
|
| 156 |
+
"git_repo_name": "https://github.com/daveey/metta.git",
|
| 157 |
+
"wandb_unique_id": "p2.fa.clean_20240425_215042_340478"
|
| 158 |
+
}
|
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