--- library_name: stable-baselines3 tags: - BipedalWalker-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: SAC results: - metrics: - type: mean_reward value: 300.53 +/- 0.76 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: BipedalWalker-v3 type: BipedalWalker-v3 --- # **SAC** Agent playing **BipedalWalker-v3** This is a trained model of a **SAC** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo
SB3: https://github.com/DLR-RM/stable-baselines3
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo sac --env BipedalWalker-v3 -orga sb3 -f logs/ python enjoy.py --algo sac --env BipedalWalker-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo sac --env BipedalWalker-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo sac --env BipedalWalker-v3 -f logs/ -orga sb3 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('buffer_size', 300000), ('ent_coef', 'auto'), ('gamma', 0.98), ('gradient_steps', 64), ('learning_rate', 0.00073), ('learning_starts', 10000), ('n_timesteps', 500000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-3, net_arch=[400, 300])'), ('tau', 0.02), ('train_freq', 64), ('use_sde', True), ('normalize', False)]) ```