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xaeroq/
ppo-MsPacman-v5

Name: ppo-MsPacman-v5
Availability: InStock

stable-baselines3

ALE/MsPacman-v5

deep-reinforcement-learning

reinforcement-learning

model-index

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PPO Agent playing ALE/MsPacman-v5

This is a trained model of a PPO agent playing ALE/MsPacman-v5 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 ALE/MsPacman-v5 -orga xaeroq -f logs/
python enjoy.py --algo ppo --env ALE/MsPacman-v5  -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 ALE/MsPacman-v5 -orga xaeroq -f logs/
rl_zoo3 enjoy --algo ppo --env ALE/MsPacman-v5  -f logs/

Training (with the RL Zoo)

python train.py --algo ppo --env ALE/MsPacman-v5 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env ALE/MsPacman-v5 -f logs/ -orga xaeroq

Hyperparameters

python

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)])

xaeroq/ppo-MsPacman-v5

PPO Agent playing ALE/MsPacman-v5

Usage (with SB3 RL Zoo)

Training (with the RL Zoo)

Hyperparameters

xaeroq/
ppo-MsPacman-v5