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--- |
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library_name: stable-baselines3 |
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tags: |
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- seals/Walker2d-v1 |
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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: SAC |
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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: seals/Walker2d-v1 |
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type: seals/Walker2d-v1 |
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metrics: |
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- type: mean_reward |
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value: 5665.26 +/- 225.00 |
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name: mean_reward |
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verified: false |
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--- |
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# **SAC** Agent playing **seals/Walker2d-v1** |
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This is a trained model of a **SAC** agent playing **seals/Walker2d-v1** |
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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 sac --env seals/Walker2d-v1 -orga HumanCompatibleAI -f logs/ |
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python -m rl_zoo3.enjoy --algo sac --env seals/Walker2d-v1 -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 sac --env seals/Walker2d-v1 -orga HumanCompatibleAI -f logs/ |
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python -m rl_zoo3.enjoy --algo sac --env seals/Walker2d-v1 -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 sac --env seals/Walker2d-v1 -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 sac --env seals/Walker2d-v1 -f logs/ -orga HumanCompatibleAI |
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``` |
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## Hyperparameters |
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```python |
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OrderedDict([('batch_size', 128), |
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('buffer_size', 100000), |
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('gamma', 0.99), |
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('learning_rate', 0.0005845844772048097), |
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('learning_starts', 1000), |
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('n_timesteps', 1000000.0), |
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('policy', 'MlpPolicy'), |
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('policy_kwargs', |
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{'log_std_init': 0.1955317469998743, |
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'net_arch': [400, 300], |
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'use_sde': False}), |
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('tau', 0.02), |
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('train_freq', 1), |
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('normalize', False)]) |
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``` |
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# Environment Arguments |
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```python |
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{'render_mode': 'rgb_array'} |
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``` |
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