PPO Agent playing MiniGrid-PutNear-6x6-N2-v0
This is a trained model of a PPO agent playing MiniGrid-PutNear-6x6-N2-v0 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
Install the RL Zoo (with SB3 and SB3-Contrib):
pip install rl_zoo3
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env MiniGrid-PutNear-6x6-N2-v0 -orga sb3 -f logs/
python -m rl_zoo3.enjoy --algo ppo --env MiniGrid-PutNear-6x6-N2-v0 -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 MiniGrid-PutNear-6x6-N2-v0 -orga sb3 -f logs/
python -m rl_zoo3.enjoy --algo ppo --env MiniGrid-PutNear-6x6-N2-v0 -f logs/
Training (with the RL Zoo)
python -m rl_zoo3.train --algo ppo --env MiniGrid-PutNear-6x6-N2-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env MiniGrid-PutNear-6x6-N2-v0 -f logs/ -orga sb3
Hyperparameters
OrderedDict([('batch_size', 64),
('clip_range', 0.2),
('ent_coef', 0.0),
('env_wrapper', 'gym_minigrid.wrappers.FlatObsWrapper'),
('gae_lambda', 0.95),
('gamma', 0.99),
('learning_rate', 0.00025),
('n_envs', 8),
('n_epochs', 10),
('n_steps', 128),
('n_timesteps', 10000000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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Evaluation results
- mean_reward on MiniGrid-PutNear-6x6-N2-v0self-reported0.61 +/- 0.33