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sb3/
a2c-MsPacmanNoFrameskip-v4

Name: a2c-MsPacmanNoFrameskip-v4
Availability: InStock

stable-baselines3

MsPacmanNoFrameskip-v4

deep-reinforcement-learning

reinforcement-learning

model-index

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A2C Agent playing MsPacmanNoFrameskip-v4

This is a trained model of a A2C agent playing MsPacmanNoFrameskip-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 a2c --env MsPacmanNoFrameskip-v4 -orga sb3 -f logs/
python enjoy.py --algo a2c --env MsPacmanNoFrameskip-v4  -f logs/

Training (with the RL Zoo)

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

sb3/a2c-MsPacmanNoFrameskip-v4

A2C Agent playing MsPacmanNoFrameskip-v4

Usage (with SB3 RL Zoo)

Training (with the RL Zoo)

Hyperparameters

sb3/
a2c-MsPacmanNoFrameskip-v4