metadata
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 803.50 +/- 323.72
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
A2C Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a A2C agent playing SpaceInvadersNoFrameskip-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 utils.load_from_hub --algo a2c --env SpaceInvadersNoFrameskip-v4 -orga sb3 -f logs/
python enjoy.py --algo a2c --env SpaceInvadersNoFrameskip-v4 -f logs/
Training (with the RL Zoo)
python train.py --algo a2c --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo a2c --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga sb3
Hyperparameters
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)])