Hands-On Reinforcement Learning with Python

Basic simulations

Let's see how to simulate a basic cart pole environment:

1. First, let's import the library:

import gym

2. The next step is to create a simulation instance using the make function:

env = gym.make('CartPole-v0')

3. Then we should initialize the environment using the reset method:

env.reset()

4. Then we can loop for some time steps and render the environment at each step:

for _ in range(1000):    env.render()    env.step(env.action_space.sample())

The complete code is as follows:

import gym env = gym.make('CartPole-v0')env.reset() for _ in range(1000):     env.render()      env.step(env.action_space.sample())

If you run the preceding program, you can see the output, which shows the cart pole environment:

OpenAI Gym provides a lot of simulation ...