Downloads · 30 days
1
25% of all-time downloads
LunarLander
DanielGigliotti/LunarLander
LunarLander is a reinforcement learning model from DanielGigliotti. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for stable-baselines3.
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
All-time downloads
4
Public
Repo size
402 KB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.zip148 KB · 34%
At a glance
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Access
- Public
- Created
- Aug 3, 2025
- Updated
- Aug 3, 2025
- SHA
- 45d44d11
Model card
From the Hugging Face model README
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 stable_baselines3 import ...
# Train the agent
# Create a (vectorized) environment
# eng = gym.make('LunarLander-v2')
env = make_vec_env('LunarLander-v2', n_envs=16)
# Define a PPO MlpPolicy architecture
model = PPO('MlpPolicy', env, verbose=True)
# Train it for 1,000,000 timesteps
model.learn(total_timesteps=1000000)
# Evaluate the agent on a new environment
# Create an evaluation environment
eval_env = Monitor(gym.make('LunarLander-v2'))
# Evaluate the model with 10 evaluation episodes and deterministic=True
mean_reward, std_reward = evaluate_policy(model, eval_env)
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- Aug 3, 2025
- Updated
- Aug 3, 2025