Update README.md

README.md

@@ -1,3 +1,53 @@
 ---
-{}
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
 ---
+
+This is a pre-trained model of a PPO agent playing CartPole-v1 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
+
+### 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 os
+
+import gymnasium as gym
+
+from huggingface_sb3 import load_from_hub
+from stable_baselines3 import PPO
+from stable_baselines3.common.evaluation import evaluate_policy
+
+# Allow the use of `pickle.load()` when downloading model from the hub
+# Please make sure that the organization from which you download can be trusted
+os.environ["TRUST_REMOTE_CODE"] = "True"
+
+# 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="sb3/demo-hf-CartPole-v1",
+    filename="ppo-CartPole-v1",
+)
+model = PPO.load(checkpoint)
+
+# Evaluate the agent and watch it
+eval_env = gym.make("CartPole-v1")
+mean_reward, std_reward = evaluate_policy(
+    model, eval_env, render=True, n_eval_episodes=5, deterministic=True, warn=False
+)
+print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
+```
+
+### Evaluation Results
+Mean_reward: 500.0
+
+