PPO Agent playing BreakoutNoFrameskip-v4

This is a trained model of a PPO 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
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env BreakoutNoFrameskip-v4 -orga sb3 -f logs/
python enjoy.py --algo ppo --env BreakoutNoFrameskip-v4 -f logs/


Training (with the RL Zoo)

python train.py --algo ppo --env BreakoutNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env BreakoutNoFrameskip-v4 -f logs/ -orga sb3


Hyperparameters

OrderedDict([('batch_size', 256),
('clip_range', 'lin_0.1'),
('ent_coef', 0.01),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('learning_rate', 'lin_2.5e-4'),
('n_envs', 8),
('n_epochs', 4),
('n_steps', 128),
('n_timesteps', 10000000.0),
('policy', 'CnnPolicy'),
('vf_coef', 0.5),
('normalize', False)])