tags: - deep-reinforcement-learning - reinforcement-learning - stable-baselines3
This is a pre-trained model of a PPO agent playing SpaceInvadersNoFrameskip using the stable-baselines3 library. It is taken from RL-trained-agents
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
pip install stable-baselines3 pip install huggingface_sb3
Then, you can use the model like this:
import gym from huggingface_sb3 import load_from_hub from stable_baselines3 import PPO from stable_baselines3.common.evaluation import evaluate_policy from stable_baselines3.common.env_util import make_atari_env from stable_baselines3.common.vec_env import VecFrameStack # Retrieve the model from the hub ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name}) ## filename = name of the model zip file from the repository checkpoint = load_from_hub(repo_id="ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4", filename="SpaceInvadersNoFrameskip-v4") print(checkpoint) model = PPO.load(checkpoint)
Mean_reward: {your_evaluation_results}
