from stable_baselines3 import PPO
import gymnasium as gym

# Load the trained model
model = PPO.load("lunar_lander_ppo_model")

# Create environment
env = gym.make('LunarLander-v2', render_mode='human')

# Test the agent
obs, _ = env.reset()
for _ in range(1000):
    action, _ = model.predict(obs, deterministic=True)
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        obs, _ = env.reset()

env.close()

Training Details

The agent was trained using PPO with the following hyperparameters:

Learning rate: 0.0003
Batch size: 64
Number of environments: 4
Gamma: 0.999

Results

The agent successfully learned to:

Control spacecraft thrust
Navigate to landing pad
Execute gentle landings
Conserve fuel efficiently

Watch it land on the moon! 🌙

Vibudhbh/lander-ppo_rl

🚀 PPO Agent for LunarLander-v2

Model Description

Performance

Usage

Training Details

Results

Vibudhbh/
lander-ppo_rl