Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use billwong1220/ppo-mlp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use billwong1220/ppo-mlp with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="billwong1220/ppo-mlp", 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 ...
from huggingface_sb3 import load_from_hub
from stable_baselines3.ppo import MlpPolicy
model = PPO(
policy="MlpPolicy",
env=env,
n_steps=1024,
batch_size=64,
n_epochs=4,
gamma=0.9999,
gae_lambda=0.98,
ent_coef=0.01,
verbose=1,
)
model.learn(total_timesteps=1000000)
model_name = "ppo-LunarLander-v2"
model.save(model_name)
...
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Evaluation results
- mean_reward on LunarLander-v2self-reported268.71 +/- 21.09