A Guide to the Gym Toolkit
OpenAI is an artificial intelligence (AI) research organization that aims to build artificial general intelligence (AGI). OpenAI provides a famous toolkit called Gym for training a reinforcement learning agent.
Let's suppose we need to train our agent to drive a car. We need an environment to train the agent. Can we train our agent in the real-world environment to drive a car? No, because we have learned that reinforcement learning (RL) is a trial-and-error learning process, so while we train our agent, it will make a lot of mistakes during learning. For example, let's suppose our agent hits another vehicle, and it receives a negative reward. It will then learn that hitting other vehicles is not a good action and will try not to perform this action again. But we cannot train the RL agent in the real-world environment by hitting other vehicles, right? That is why we use simulators and train the RL agent in the simulated environments.
There are many toolkits that provide a simulated environment for training an RL agent. One such popular toolkit is Gym. Gym provides a variety of environments for training an RL agent ranging from classic control tasks to Atari game environments. We can train our RL agent to learn in these simulated environments using various RL algorithms. In this chapter, first, we will install Gym and then we will explore various Gym environments. We will also get hands-on with the concepts we have learned in the previous chapter by experimenting with the Gym environment.
Throughout the book, we will use the Gym toolkit for building and evaluating reinforcement learning algorithms, so in this chapter, we will make ourselves familiar with the Gym toolkit.
In this chapter, we will learn about the following topics:
- Setting up our machine
- Installing Anaconda and Gym
- Understanding the Gym environment
- Generating an episode in the Gym environment
- Exploring more Gym environments
- Cart-Pole balancing with the random agent
- An agent playing the Tennis game
