--- library_name: stable-baselines3 tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - metrics: - type: mean_reward value: 17.10 +/- 2.70 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 --- # **A2C** Agent playing **PongNoFrameskip-v4** This is a trained model of a **A2C** agent playing **PongNoFrameskip-v4** 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 a2c --env PongNoFrameskip-v4 -orga sb3 -f logs/ python enjoy.py --algo a2c --env PongNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo a2c --env PongNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env PongNoFrameskip-v4 -f logs/ -orga sb3 ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.01), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('frame_stack', 4), ('n_envs', 16), ('n_timesteps', 10000000.0), ('policy', 'CnnPolicy'), ('policy_kwargs', 'dict(optimizer_class=RMSpropTFLike, ' 'optimizer_kwargs=dict(eps=1e-5))'), ('vf_coef', 0.25), ('normalize', False)]) ```