metadata
library_name: stable-baselines3
tags:
- Acrobot-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: '-79.80 +/- 5.44'
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Acrobot-v1
type: Acrobot-v1
A2C Agent playing Acrobot-v1
This is a trained model of a A2C agent playing Acrobot-v1 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 Acrobot-v1 -orga sb3 -f logs/
python enjoy.py --algo a2c --env Acrobot-v1 -f logs/
Training (with the RL Zoo)
python train.py --algo a2c --env Acrobot-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env Acrobot-v1 -f logs/ -orga sb3
Hyperparameters
OrderedDict([('ent_coef', 0.0),
('n_envs', 16),
('n_timesteps', 500000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])