Artificial neural networks are computational systems that provide us with important tools to solve challenging machine learning tasks, ranging from image recognition to speech translation. Recent breakthroughs, such as Google DeepMind's AlphaGo defeating the best Go players or Carnegie Mellon University's Libratus defeating the world's best professional poker players, have demonstrated the advancement in the algorithms; these algorithms learn a narrow intelligence like a human would and achieve superhuman-level performance. In plain speech, artificial neural networks are a loose representation of the human brain that we can program in a computer; to be precise, it's an approach inspired by our knowledge of the functions of the human brain. A key concept of neural networks is to create a representation space of the input data and then solve the problem in that space; that is, warping the data from its current state in such a way that it can be represented in a different state where it can solve the concerned problem statement (say, a classification or regression). Deep learning means multiple hidden representations, that is, a neural network with many layers to create more effective representations of the data. Each layer refines the information received from the previous one.

Reinforcement learning, on the other hand, is another wing of machine learning, which is a technique to learn any kind of activity that follows a sequence of actions. A reinforcement learning agent gathers the information from the environment and creates a representation of the states; it then performs an action that results in a new state and a reward (that is, quantifiable feedback from the environment telling us whether the action was good or bad). This phenomenon continues until the agent is able to improve the performance beyond a certain threshold, that is, maximizing the expected value of the rewards. At each step, these actions can be chosen randomly, can be fixed, or can be supervised using a neural network. The supervision of predicting action using a deep neural network opens a new domain, called deep reinforcement learning. This forms the base of AlphaGo, Libratus, and many other breakthrough research in the field of artificial intelligence.

We will cover the following topics in this chapter: