Added LunarLander-v2 video

README.md

@@ -1,51 +1,3 @@
 ---
-tags:
-- deep-reinforcement-learning
-- reinforcement-learning
-- stable-baselines3
+{}
 ---
-# "Beyko7/ppo-LunarLander-v2"
-
-This is a pre-trained model of a PPO agent playing LunarLander-v2 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 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="Beyko7/ppo-LunarLander-v2", filename="LunarLander-v2.zip")
-model = PPO.load(checkpoint)
-
-# Evaluate the agent
-eval_env = gym.make('LunarLander-v2')
-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: 248.30 +/- 23.32882124373712