--- library_name: stable-baselines3 tags: - MountainCar-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: MountainCar-v0 type: MountainCar-v0 metrics: - type: mean_reward value: -110.70 +/- 19.61 name: mean_reward verified: false --- # **A2C** Agent playing **MountainCar-v0** This is a trained model of a **A2C** agent playing **MountainCar-v0** 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 a2c --env MountainCar-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env MountainCar-v0 -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 a2c --env MountainCar-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env MountainCar-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env MountainCar-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env MountainCar-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.0), ('n_envs', 16), ('n_timesteps', 1000000.0), ('normalize', True), ('policy', 'MlpPolicy'), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```