metadata
license: mpl-2.0
tags:
- reinforcement-learning
- stable-baselines3
- enduro
- atari
- sb3
- ppo
- control
model-index:
- name: Enduro-v5-PPO
parameters: 2.2M
results:
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Enduro-v5
type: gymnasium
metrics:
- type: mean_reward
value: 599.54 +/- 131.49
PPO Agent for Enduro-v5
This is a Proximal Policy Optimization (PPO) agent trained on the Enduro-v5 environment using Stable Baselines 3.
Hyperparameters
See config.json for details.
Requirements
- Python: 3.10
Dependencies
gymnasium==1.0.0
ale_py==0.10.1
gymnasium[atari]
torch==2.4.0
stable_baselines3==2.4.1
opencv-python==25.0.1
How to Load
from huggingface_hub import hf_hub_download
from stable_baselines3 import PPO
model_path = hf_hub_download(repo_id="lucasschott/Enduro-v5-PPO", filename="model.zip")
agent = PPO.load(model_path)