Algorithms that parse data, learn from that data, and then apply what they've learned to make informed decisions. I'm sure you are asking yourself, how can a program or algorithm make decisions and learn from data, doesn't every program need to be programmed? Not if the program was trained to learn from and adapt to data. In the case of machine learning the algorithm is not explicitly programmed, rather the model is "trained" using historical and present data in order to make future decisions and prediction. The more data available for training, the more accurate the predictions are.
In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. ISBN 9781608454921, 103 pages.
In machine learning, the problem of reinforcement learning is concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioral decisions. This tutorial will introduce the fundamental concepts and vocabulary that underlie this field of study. It will also review recent advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology.