Create README.md

README.md

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+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.22 +/- 0.12
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **PandaReachDense-v3**
+This is a trained model of a **PPO** agent playing **PandaReachDense-v3**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+
+from stable_baselines3 import PPO
+from huggingface_sb3 import load_from_hub, package_to_hub
+from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
+
+env_id = "PandaReachDense-v3"
+env = gym.make(env_id)
+env = make_vec_env(env_id, n_envs=4)
+env = VecNormalize(env, training=True, norm_obs=True, norm_reward=True, gamma=0.5, epsilon=1e-10, norm_obs_keys=None)
+
+model =  PPO("MultiInputPolicy", env, verbose=1)
+model.learn(1_000_000)
+
+eval_env = DummyVecEnv([lambda: gym.make("PandaReachDense-v3")])
+eval_env = VecNormalize.load("vec_normalize.pkl", eval_env)
+eval_env.render_mode = "rgb_array"
+eval_env.training = False
+# reward normalization is not needed at test time
+eval_env.norm_reward = False
+
+
+model = PPO.load("Slay-PandaReachDense-v3")
+mean_reward, std_reward = evaluate_policy(model, eval_env)
+print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
+...
+```