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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: 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: 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: 979.19 +/- 4.29 |
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name: mean_reward |
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verified: false |
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--- |
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# **ARS** Agent playing **AntBulletEnv-v0** |
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This is a trained model of a **ARS** 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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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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## Usage (with SB3 RL Zoo) |
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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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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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# Download model and save it into the logs/ folder |
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python -m rl_zoo3.load_from_hub --algo ars --env AntBulletEnv-v0 -orga qgallouedec -f logs/ |
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python -m rl_zoo3.enjoy --algo ars --env AntBulletEnv-v0 -f logs/ |
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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 AntBulletEnv-v0 -orga qgallouedec -f logs/ |
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python -m rl_zoo3.enjoy --algo ars --env AntBulletEnv-v0 -f logs/ |
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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 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 ars --env AntBulletEnv-v0 -f logs/ -orga qgallouedec |
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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', 32), |
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('n_envs', 1), |
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('n_timesteps', 75000000.0), |
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('n_top', 32), |
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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=[128, 64])'), |
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('zero_policy', False), |
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('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) |
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``` |
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