--- library_name: stable-baselines3 tags: - InvertedPendulum-v2 - InvertedPendulum-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: InvertedPendulum-v2 type: InvertedPendulum-v2 metrics: - type: mean_reward value: 1000.00 +/- 0.00 name: mean_reward verified: false --- # **PPO** Agent playing **InvertedPendulum-v2** This is a trained model of a **PPO** agent playing **InvertedPendulum-v2** 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 Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo --env InvertedPendulum-v2 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo --env InvertedPendulum-v2 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo --env InvertedPendulum-v2 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo --env InvertedPendulum-v2 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env InvertedPendulum-v2 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env InvertedPendulum-v2 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('clip_range', 0.4), ('ent_coef', 1.37976e-07), ('gae_lambda', 0.9), ('gamma', 0.999), ('learning_rate', 0.000222425), ('max_grad_norm', 0.3), ('n_envs', 1), ('n_epochs', 5), ('n_steps', 32), ('n_timesteps', 1000000.0), ('normalize', True), ('policy', 'MlpPolicy'), ('vf_coef', 0.19816), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```