Update README.md

README.md

@@ -1,7 +1,56 @@
+#@title
 ---
 tags:
+- bipedal
+- walker
 - deep-reinforcement-learning
 - reinforcement-learning
 - stable-baselines3
 ---
-# TODO: Fill this model card
+# PPO BipedalWalker v3
+
+This is a pre-trained model of a PPO agent playing BipedalWalker-v3 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
+
+<video loop="" autoplay="" controls="" src="https://huggingface.co/mrm8488/ppo-BipedalWalker-v3/resolve/main/output.mp4"></video>
+
+### 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
+
+# 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="mrm8488/ppo-BipedalWalker-v3", filename="bipedalwalker-v3.zip")
+model = PPO.load(checkpoint)
+
+# Evaluate the agent
+eval_env = gym.make('{environment}')
+mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
+print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
+ 
+# Watch the agent play
+obs = env.reset()
+for i in range(1000):
+    action, _state = model.predict(obs)
+    obs, reward, done, info = env.step(action)
+    env.render()
+    if done:
+        obs = env.reset()
+env.close()
+```
+
+### Evaluation Results
+Mean_reward: 213.55 +/- 113.82