Hands-On Q-Learning with Python

Getting started with the CartPole task

Let's get an instance of CartPole-v1 running.

We import the necessary package and build the environment with the call to gym.make(), as shown in the following code snippet:

import gymenv = gym.make('CartPole-v1')env.reset()done = Falsewhile not done:    env.render()    # Take a random action    observation, reward, done, info = env.step(env.action_space.sample()) 

We run a loop that renders the environment for a maximum of 1,000 steps. It takes random actions at each timestep using env.step().

Here's the output displaying the CartPole-v1 environment:

You should see a small window pop up with the CartPole environment ...