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: 289.96 +/- 22.59
            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)

import gym
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub

checkpoint = load_from_hub(
    repo_id="dmenini/ppo-LunarLander-v2",
    filename="ppo-LunarLander-v2.zip"
)

model = PPO.load(checkpoint)

env = gym.make("LunarLander-v2")

# Evaluate the agent and watch it
eval_env = gym.make("LunarLander-v2")
mean_reward, std_reward = evaluate_policy(
    model, eval_env, render=True, n_eval_episodes=5, deterministic=True, warn=False
)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")