Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use mavleo96/ppo-lunarlander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use mavleo96/ppo-lunarlander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="mavleo96/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)
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()
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Evaluation results
- mean_reward on LunarLander-v2self-reported262.43 +/- 18.65