Deep Q-learning
Here comes the fun part—the brain design of our AI Atari player. The core algorithm is based on deep reinforcement learning or deep RL. In order to understand it better, some basic mathematical formulations are required. Deep RL is a perfect combination of deep learning and traditional reinforcement learning. Without understanding the basic concepts about reinforcement learning, it is difficult to apply deep RL correctly in real applications, for example, it is possible that someone may try to use deep RL without defining state space, reward, and transition properly.
Well, don't be afraid of the difficulty of the formulations. We only need high school-level mathematics, and will not go deep into the mathematical proofs of why traditional reinforcement learning algorithms work. The goal of this chapter is to learn the basic Q-learning algorithm, to know how to extend it into the deep Q-learning algorithm (DQN), and to understand the intuition behind these algorithms. Besides...
