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.
Let's see how to simulate a basic cart pole environment:
- First, let's import the library:
import gym
- The next step is to create a simulation instance using the
makefunction:
env = gym.make('CartPole-v0')- Then we should initialize the environment using the
resetmethod:
env.reset()
- 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...