OpenAI Gym

With OpenAI Gym, we can simulate a variety of environments and develop, evaluate, and compare RL algorithms. Let's now understand how to use Gym.

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_inrange(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 environments for training, evaluating, and building our agents. We can check the available environments...