halaGPT edition
Safe lab preview
This is a sanitized, read-only preview. Nothing executes in this page.
Read-only
Optional self-reporting helps you remember what you practiced and never gates course completion.
Use this guided lab to apply the lesson concepts.
Your goal is to train the RL agent to control Mountain Car in OpenAI Environment.
Figure description: Mountain Car
Mountain Car environment consists of the car trapped inside a valley. Your goal is to jump out of the valley and reach the flag. The actions you can perform are to accelerate to the left, to the right, or do nothing. You can observe position of the car along x-axis, and velocity.
Start the lab by opening MountainCar.ipynb
You should learn throughout this lab that adopting RL algorithms to a new environment is often quite straightforward, because the Gymnasium has the same interface for all environments, and algorithms as such do not largely depend on the nature of the environment. You can even restructure the Python code in such a way as to pass any environment to RL algorithm as a parameter.
import gymnasium as gym
env = gym.make("MountainCar-v0", render_mode="rgb_array")state, _ = env.reset()
while True:
env.render()
action = env.action_space.sample()
state, reward, terminated, truncated, info = env.step(action)
done = terminated or truncated
if done:
break## Lost of code hereenv.close()Outputs, execution counts, widgets, and active content were removed during import. Run notebooks only in an external environment you trust.