PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
Model parameters
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)
Trained for 10^6 steps using
steps = 1e6
model.learn(total_timesteps=int(steps))
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-reported278.97 +/- 18.80