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Sahilsea/ppo-LunarLander-v3
ppo-LunarLander-v3 is a reinforcement learning model from Sahilsea. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for stable-baselines3.
This is a trained model of a PPO agent playing LunarLander-v3 using the stable-baselines3 library.
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From the Hugging Face model README
This is a trained model of a PPO agent playing LunarLander-v3 using the stable-baselines3 library.
TODO: Add your code
import gymnasium as gym
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
from gymnasium.wrappers import RecordVideo
from IPython.display import Video, display
import glob
import os
# 1. Download and load your checkpoint
checkpoint = load_from_hub(
repo_id="Sahilsea/ppo-LunarLander-v3",
filename="ppo-LunarLander-v3.zip",
)
model = PPO.load(checkpoint)
# 2. Set up the environment with a video recorder wrapper
video_folder = "./videos"
env = gym.make("LunarLander-v3", render_mode="rgb_array")
env = RecordVideo(env, video_folder=video_folder, episode_trigger=lambda e: True)
# 3. Run one full landing episode
obs, info = env.reset()
done = False
while not done:
action, _states = model.predict(obs, deterministic=True)
obs, reward, terminated, truncated, info = env.step(action)
done = terminated or truncated
env.close()
# 4. Display the recorded MP4 file
mp4_files = glob.glob(f"{video_folder}/*.mp4")
if mp4_files:
display(Video(mp4_files[-1], embed=True, html_attributes="controls autoplay loop"))
else:
print("No video file found.")
...