Springer Reinforcement Learning - Engineering and Computer Science

Explore the fundamentals of machine learning with Reinforcement Learning, part of the Springer International Series in Engineering and Computer Science. This text examines the process of learning a mapping from situations to actions to maximize a scalar reward or reinforcement signal. Unlike many other forms of machine learning where a learner is told which action to take, this book focuses on the trial-and-error search required to discover which actions yield the highest rewards. It addresses the complex challenges of delayed rewards, where current actions influence not only immediate outcomes but also future situations and all subsequent rewards. This resource is designed for those studying how agents navigate environments through discovery rather than direct instruction. By understanding these core distinguishing features, readers gain insight into the mechanics of how agents learn to optimize behavior in dynamic settings.