Create README.md

README.md

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+---
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+---
+# ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4
+
+<video src="https://huggingface.co/ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4/blob/main/output.mp4" controls autoplay loop/>
+
+This is a pre-trained model of a PPO agent playing SpaceInvadersNoFrameskip using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library. It is taken from [RL-trained-agents](https://github.com/DLR-RM/rl-trained-agents)
+
+### Usage (with Stable-baselines3)
+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:
+
+```python
+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)
+```
+
+### Evaluation Results
+Mean_reward: {your_evaluation_results}
+