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--- |
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library_name: stable-baselines3 |
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tags: |
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- seals/MountainCar-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: PPO |
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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/MountainCar-v0 |
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type: seals/MountainCar-v0 |
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metrics: |
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- type: mean_reward |
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value: -97.00 +/- 8.26 |
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name: mean_reward |
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verified: false |
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--- |
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# **PPO** Agent playing **seals/MountainCar-v0** |
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This is a trained model of a **PPO** agent playing **seals/MountainCar-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 ppo --env seals/MountainCar-v0 -orga HumanCompatibleAI -f logs/ |
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python -m rl_zoo3.enjoy --algo ppo --env seals/MountainCar-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 ppo --env seals/MountainCar-v0 -orga HumanCompatibleAI -f logs/ |
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python -m rl_zoo3.enjoy --algo ppo --env seals/MountainCar-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 ppo --env seals/MountainCar-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 ppo --env seals/MountainCar-v0 -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', 512), |
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('clip_range', 0.2), |
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('ent_coef', 6.4940755116195606e-06), |
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('gae_lambda', 0.98), |
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('gamma', 0.99), |
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('learning_rate', 0.0004476103728105138), |
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('max_grad_norm', 1), |
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('n_envs', 16), |
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('n_epochs', 20), |
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('n_steps', 256), |
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('n_timesteps', 1000000.0), |
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('normalize', |
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{'gamma': 0.99, 'norm_obs': False, 'norm_reward': True}), |
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('policy', 'MlpPolicy'), |
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('policy_kwargs', |
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{'activation_fn': <class 'torch.nn.modules.activation.Tanh'>, |
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'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, |
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'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}), |
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('vf_coef', 0.25988158989488963), |
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('normalize_kwargs', |
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{'norm_obs': {'gamma': 0.99, |
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'norm_obs': False, |
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'norm_reward': True}, |
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'norm_reward': 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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