gymnasium_lunar_lander_random_agent_simulation.py

1import gymnasium as gym
2
3env = gym.make("LunarLander-v3", render_mode="human")
4observation, info = env.reset()
5
6for _ in range(1000):
7    action = env.action_space.sample()  # agent policy that uses the observation and info
8    observation, reward, terminated, truncated, info = env.step(action)
9
10    if terminated or truncated:
11        observation, info = env.reset()
12
13env.close()