metadata
library_name: stable-baselines3
pipeline_tag: robotics
tags:
- LiftCube-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
- robotics
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LiftCube-v0
type: LiftCube-v0
metrics:
- type: mean_reward
value: '-327.63 +/- 25.14'
name: mean_reward
verified: false
PPO Agent playing LiftCube-v0
This is a trained model of a PPO agent playing LiftCube-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 LiftCube-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ppo --env LiftCube-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 LiftCube-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ppo --env LiftCube-v0 -f logs/
Training (with the RL Zoo)
python -m rl_zoo3.train --algo ppo --env LiftCube-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env LiftCube-v0 -f logs/ -orga qgallouedec
Hyperparameters
OrderedDict([('n_envs', 16),
('n_timesteps', 10000000.0),
('policy', 'MultiInputPolicy'),
('use_sde', True),
('normalize', False)])
Environment Arguments
{'observation_mode': 'state', 'render_mode': 'rgb_array'}