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ars-ReacherBulletEnv-v0-2337213954

Reinforcement Learning
stable-baselines3
ReacherBulletEnv-v0
deep-reinforcement-learning
Eval Results
Model card Files Community
Quentin Gallouédec commited on
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  1. .gitattributes +1 -0
  2. README.md +77 -0
  3. args.yml +79 -0
  4. ars-ReacherBulletEnv-v0.zip +3 -0
  5. ars-ReacherBulletEnv-v0/_stable_baselines3_version +1 -0
  6. ars-ReacherBulletEnv-v0/data +164 -0
  7. ars-ReacherBulletEnv-v0/policy.pth +3 -0
  8. ars-ReacherBulletEnv-v0/pytorch_variables.pth +3 -0
  9. ars-ReacherBulletEnv-v0/system_info.txt +7 -0
  10. config.yml +23 -0
  11. env_kwargs.yml +1 -0
  12. replay.mp4 +3 -0
  13. results.json +1 -0
  14. train_eval_metrics.zip +3 -0
  15. vec_normalize.pkl +3 -0
.gitattributes CHANGED
@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,77 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - ReacherBulletEnv-v0
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - stable-baselines3
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+ model-index:
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+ - name: ARS
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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: ReacherBulletEnv-v0
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+ type: ReacherBulletEnv-v0
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+ metrics:
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+ - type: mean_reward
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+ value: -4.18 +/- 11.26
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **ARS** Agent playing **ReacherBulletEnv-v0**
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+ This is a trained model of a **ARS** agent playing **ReacherBulletEnv-v0**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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+ and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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+
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+ The RL Zoo is a training framework for Stable Baselines3
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+ reinforcement learning agents,
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+ with hyperparameter optimization and pre-trained agents included.
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+
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+ ## Usage (with SB3 RL Zoo)
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+
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+ RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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+ SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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+ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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+
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+ Install the RL Zoo (with SB3 and SB3-Contrib):
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+ ```bash
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+ pip install rl_zoo3
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+ ```
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+
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+ ```
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+ # Download model and save it into the logs/ folder
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+ python -m rl_zoo3.load_from_hub --algo ars --env ReacherBulletEnv-v0 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo ars --env ReacherBulletEnv-v0 -f logs/
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+ ```
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+
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+ If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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+ ```
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+ python -m rl_zoo3.load_from_hub --algo ars --env ReacherBulletEnv-v0 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo ars --env ReacherBulletEnv-v0 -f logs/
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+ ```
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+
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+ ## Training (with the RL Zoo)
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+ ```
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+ python -m rl_zoo3.train --algo ars --env ReacherBulletEnv-v0 -f logs/
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+ # Upload the model and generate video (when possible)
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+ python -m rl_zoo3.push_to_hub --algo ars --env ReacherBulletEnv-v0 -f logs/ -orga qgallouedec
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+ ```
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+
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+ ## Hyperparameters
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+ ```python
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+ OrderedDict([('alive_bonus_offset', 0),
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+ ('delta_std', 0.03),
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+ ('learning_rate', 0.02),
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+ ('n_delta', 8),
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+ ('n_envs', 1),
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+ ('n_timesteps', 1000000.0),
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+ ('n_top', 8),
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+ ('normalize', 'dict(norm_obs=True, norm_reward=False)'),
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+ ('policy', 'MlpPolicy'),
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+ ('policy_kwargs', 'dict(net_arch=[64, 64])'),
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+ ('zero_policy', False),
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+ ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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+ ```
args.yml ADDED
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+ !!python/object/apply:collections.OrderedDict
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+ - - - algo
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+ - ars
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+ - - device
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+ - auto
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+ - - env
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+ - ReacherBulletEnv-v0
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+ - - env_kwargs
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+ - null
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+ - - eval_episodes
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+ - 5
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+ - - eval_freq
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+ - 25000
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+ - - gym_packages
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+ - []
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+ - - hyperparams
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+ - null
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+ - - log_folder
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+ - logs
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+ - - log_interval
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+ - -1
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+ - - progress
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+ - 2337213954
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+ - - storage
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+ - - study_name
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+ - null
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+ - - tensorboard_log
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+ - runs/ReacherBulletEnv-v0__ars__2337213954__1671645640
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+ - - track
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+ - true
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+ - - trained_agent
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+ - ''
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+ - - truncate_last_trajectory
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+ - true
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+ - - uuid
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+ - false
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+ - - vec_env
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+ - dummy
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+ - - verbose
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+ - 1
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+ - - wandb_entity
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+ - openrlbenchmark
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+ - - wandb_project_name
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+ - sb3
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+ - - yaml_file
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+ - null
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