PPO('MlpPolicy', n_steps = 1024, batch_size = 64, n_epochs = 4, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01) Agent playing LunarLander-v2
This is a trained model of a PPO('MlpPolicy', n_steps = 1024, batch_size = 64, n_epochs = 4, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01) 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
...
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Evaluation results
- mean_reward on LunarLander-v2self-reported264.86 +/- 23.85