ppo-LunarLander-v2

#@title

library_name: stable-baselines3 tags:

• LunarLander-v2
• deep-reinforcement-learning
• reinforcement-learning
• stable-baselines3 model-index:
• name: PPO results:
  • metrics:
    • type: mean_reward value: 290.76 +/- 18.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2

---

{name_of_your_repo}

This is a pre-trained model of a {algo} agent playing {environment} using the 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:

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="{repo_id}", filename="{filename}.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: {your_evaluation_results}

Demo

<video src="https://huggingface.co/ncduy/ppo-LunarLander-v2/resolve/main/output.mp4" controls autoplay loop></video>