Building a Checkers Gaming Agent Using Deep Q-Learning

One of the most intriguing learning methods in machine learning literature is reinforcement learning. Through reward and punishment, an agent learns to reach a given goal by selecting an action from a set of actions in a known or unknown environment. Reinforcement learning, unlike supervised and unsupervised learning techniques, does not require any initial data. In this article, we will demonstrate how to implement a version of the reinforcement learning technique Deep Q-Learning, to create an AI agent capable of playing Checkers at a decent level. Deep reinforcement learning is a branch of machine learning that combines deep learning and reinforcement learning (RL).