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.
OpenAI Gym
Basic simulations
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 make function:
env = gym.make('CartPole-v0')
- Then we should initialize the environment using the reset method:
env.reset()
- 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...