In previous chapters, we looked at supervised learning techniques such as regression and classification, and unsupervised learning techniques such as GANs, autoencoders and generative models. In the case of supervised learning, we train the network with the expected input and output and expect it to predict the output given a new input. In the case of unsupervised learning, we show the network some input and expect it to learn the structure of the data so that it can apply this knowledge to a new input.
In this chapter, we will learn about reinforcement learning, or more specifically deep reinforcement learning, that is, the application of deep neural networks to reinforcement learning. Reinforcement learning has its roots in behavioral psychology. An agent is trained by rewarding it for correct behavior and punishing it for incorrect behavior. In the context of deep reinforcement learning, a network is shown some input and is given a positive or negative reward...