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a2c-AntBulletEnv-v0-2794615594

Reinforcement Learning
stable-baselines3
AntBulletEnv-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 +81 -0
  3. a2c-AntBulletEnv-v0.zip +3 -0
  4. a2c-AntBulletEnv-v0/_stable_baselines3_version +1 -0
  5. a2c-AntBulletEnv-v0/data +106 -0
  6. a2c-AntBulletEnv-v0/policy.optimizer.pth +3 -0
  7. a2c-AntBulletEnv-v0/policy.pth +3 -0
  8. a2c-AntBulletEnv-v0/pytorch_variables.pth +3 -0
  9. a2c-AntBulletEnv-v0/system_info.txt +7 -0
  10. args.yml +79 -0
  11. config.yml +31 -0
  12. env_kwargs.yml +1 -0
  13. replay.mp4 +3 -0
  14. results.json +1 -0
  15. train_eval_metrics.zip +3 -0
  16. vec_normalize.pkl +3 -0
.gitattributes CHANGED
@@ -32,3 +32,4 @@ saved_model/**/* 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
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - AntBulletEnv-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: A2C
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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: AntBulletEnv-v0
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+ type: AntBulletEnv-v0
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+ metrics:
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+ - type: mean_reward
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+ value: 2556.84 +/- 67.09
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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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+ # **A2C** Agent playing **AntBulletEnv-v0**
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+ This is a trained model of a **A2C** agent playing **AntBulletEnv-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 a2c --env AntBulletEnv-v0 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo a2c --env AntBulletEnv-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 a2c --env AntBulletEnv-v0 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo a2c --env AntBulletEnv-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 a2c --env AntBulletEnv-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 a2c --env AntBulletEnv-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([('ent_coef', 0.0),
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+ ('gae_lambda', 0.9),
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+ ('gamma', 0.99),
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+ ('learning_rate', 'lin_0.00096'),
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+ ('max_grad_norm', 0.5),
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+ ('n_envs', 4),
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+ ('n_steps', 8),
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+ ('n_timesteps', 2000000.0),
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+ ('normalize', True),
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+ ('normalize_advantage', False),
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+ ('policy', 'MlpPolicy'),
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+ ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'),
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+ ('use_rms_prop', True),
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+ ('use_sde', True),
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+ ('vf_coef', 0.4),
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+ ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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+ ```
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