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Upload README.md with huggingface_hub

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  1. README.md +1 -61
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@@ -5,6 +5,7 @@ tags:
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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:
@@ -20,64 +21,3 @@ model-index:
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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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- # **PPO** Agent playing **HumanoidStandup-v2**
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- This is a trained model of a **PPO** agent playing **HumanoidStandup-v2**
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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 ppo --env HumanoidStandup-v2 -orga qgallouedec -f logs/
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- python -m rl_zoo3.enjoy --algo ppo --env HumanoidStandup-v2 -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 ppo --env HumanoidStandup-v2 -orga qgallouedec -f logs/
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- python -m rl_zoo3.enjoy --algo ppo --env HumanoidStandup-v2 -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 ppo --env HumanoidStandup-v2 -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 HumanoidStandup-v2 -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([('batch_size', 32),
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- ('clip_range', 0.3),
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- ('ent_coef', 3.62109e-06),
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- ('gae_lambda', 0.9),
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- ('gamma', 0.99),
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- ('learning_rate', 2.55673e-05),
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- ('max_grad_norm', 0.7),
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- ('n_envs', 1),
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- ('n_epochs', 20),
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- ('n_steps', 512),
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- ('n_timesteps', 10000000.0),
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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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- ('vf_coef', 0.430793),
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- ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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- ```
 
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  - deep-reinforcement-learning
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  - reinforcement-learning
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  - stable-baselines3
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+ - HumanoidStandup-v4
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  model-index:
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  - name: PPO
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  results:
 
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  name: mean_reward
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  verified: false
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  ---