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--- |
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library_name: stable-baselines3 |
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tags: |
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- Walker2DBulletEnv-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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- metrics: |
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- type: mean_reward |
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value: 2120.20 +/- 6.34 |
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name: mean_reward |
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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: Walker2DBulletEnv-v0 |
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type: Walker2DBulletEnv-v0 |
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--- |
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# **PPO** Agent playing **Walker2DBulletEnv-v0** |
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This is a trained model of a **PPO** agent playing **Walker2DBulletEnv-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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``` |
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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 Walker2DBulletEnv-v0 -orga sb3 -f logs/ |
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python enjoy.py --algo ppo --env Walker2DBulletEnv-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 train.py --algo ppo --env Walker2DBulletEnv-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 Walker2DBulletEnv-v0 -f logs/ -orga sb3 |
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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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('clip_range', 'lin_0.4'), |
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('ent_coef', 0.0), |
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('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'), |
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('gae_lambda', 0.92), |
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('gamma', 0.99), |
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('learning_rate', 3e-05), |
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('max_grad_norm', 0.5), |
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('n_envs', 16), |
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('n_epochs', 20), |
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('n_steps', 512), |
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('n_timesteps', 2000000.0), |
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('normalize', True), |
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('policy', 'MlpPolicy'), |
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('policy_kwargs', |
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'dict(log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, ' |
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'net_arch=[dict(pi=[256, 256], vf=[256, 256])] )'), |
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('sde_sample_freq', 4), |
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('use_sde', True), |
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('vf_coef', 0.5), |
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('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) |
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``` |
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