Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use HamzaChera/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use HamzaChera/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="HamzaChera/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)
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.env_util import make_vec_env
import gymnasium as gym
# Load the model from the Hub
checkpoint = load_from_hub(
repo_id="HamzaChera/ppo-LunarLander-v2",
filename="ppo-LunarLander-v2.zip",
)
model = PPO.load(checkpoint)
# Create the environment
env = make_vec_env("LunarLander-v2", n_envs=1)
obs = env.reset()
while True:
action, _states = model.predict(obs, deterministic=True)
obs, rewards, dones, info = env.step(action)
env.render()
...
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Evaluation results
- mean_reward on LunarLander-v2self-reported262.89 +/- 16.52