Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use dmenini/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use dmenini/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="dmenini/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
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}")
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Evaluation results
- mean_reward on LunarLander-v2self-reported289.96 +/- 22.59