README.md

metadata

library_name: stable-baselines3
tags:
  - LunarLander-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: PPO
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: LunarLander-v2
          type: LunarLander-v2
        metrics:
          - type: mean_reward
            value: 262.43 +/- 18.65
            name: mean_reward
            verified: false

PPO Agent playing LunarLander-v2

This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.

Usage (with Stable-baselines3)

TODO: Add your code

from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
import gym

# Define model repo_id and filename
repo_id = "mavleo96/rl-bots"  # Change this to the actual repo if different
filename = "ppo-LunarLander-v2.zip"

# Load the model from the Hugging Face Hub
model = load_from_hub(repo_id, filename, model_class=PPO)

# Create the environment
env = gym.make("LunarLander-v2")

# Run a few episodes
obs = env.reset()
for _ in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()
    if done:
        obs = env.reset()

env.close()