Reinforcement Learning
stable-baselines3
deep-reinforcement-learning
LunarLander-v2
Eval Results (legacy)
Instructions to use ImaghT/ppo-LunarLander-v3-optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use ImaghT/ppo-LunarLander-v3-optimized with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="ImaghT/ppo-LunarLander-v3-optimized", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent for LunarLander-v3 (Optimized)
This is a pre-trained model for LunarLander-v3 using Stable-Baselines3.
Usage
import gymnasium as gym
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.vec_env import VecNormalize
# Load the environment
env = make_vec_env("LunarLander-v3", n_envs=1)
env = VecNormalize.load("vec_normalize.pkl", env)
env.training = False
env.norm_reward = False
# Load the model
model = PPO.load("ppo_lunar_optimized", env=env)
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Evaluation results
- mean_reward on LunarLander-v2self-reported273 +/- 9.50