Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use linker81/PPO-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use linker81/PPO-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="linker81/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)
model = PPO( policy = 'MlpPolicy', env = env, n_steps = 1024, batch_size = 64, n_epochs = 10, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01, verbose=1)
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Evaluation results
- mean_reward on LunarLander-v2self-reported279.25 +/- 16.69