Reinforcement Learning: An Introduction

"Reinforcement learning is learning what to do how to map situations to actions so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward but also the next situation and, through that, all subsequent rewards. These two characteristics trial-and-error search and delayed reward are the two most important distinguishing features of reinforcement learning."

• Authors: Richard S. Sutton, Andrew G. Barto
• Keywords: Artificial Intelligence, Machine Learning