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A2C Agent playing BreakoutNoFrameskip-v4

This is a trained model of a A2C agent playing BreakoutNoFrameskip-v4 using the stable-baselines3 library and the RL 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):

pip install rl_zoo3
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo a2c --env BreakoutNoFrameskip-v4 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env BreakoutNoFrameskip-v4  -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 BreakoutNoFrameskip-v4 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env BreakoutNoFrameskip-v4  -f logs/

Training (with the RL Zoo)

python -m rl_zoo3.train --algo a2c --env BreakoutNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env BreakoutNoFrameskip-v4 -f logs/ -orga qgallouedec

Hyperparameters

OrderedDict([('ent_coef', 0.01),
             ('env_wrapper',
              [{'stable_baselines3.common.atari_wrappers.AtariWrapper': {'action_repeat_probability': 0.25}}]),
             ('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)])
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