Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Eugene-Bond/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Eugene-Bond/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Eugene-Bond/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 typing import Callable
def linear_schedule(initial_value: float) -> Callable[[float], float]:
def func(progress_remaining: float) -> float:
return progress_remaining * initial_value
return func
model = PPO(policy="MlpPolicy", env=env, verbose=1, n_epochs=10, learning_rate=linear_schedule(0.005), n_steps=1500)
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Evaluation results
- mean_reward on LunarLander-v2self-reported282.88 +/- 14.89