Reinforcement Learning: An Introduction
This introductory textbook on reinforcement learning is targeted toward engineers and scientists in artificial intelligence, operations research, neural networks, and control systems, and we hope it will also be of interest to psychologists and neuroscientists.
Algorithms for Reinforcement Learning
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner’s predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms’ merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book we focus on those algorithms of reinforcement learning which build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
RL-Community.org is currently the home of some existing and upcoming reinforcement learning projects, listed below. The plan is that this website and domain are open for people in the community to use to help advertise and distribute their projects.
RL-Glue (Reinforcement Learning Glue) provides a standard interface that allows you to connect reinforcement learning agents, environments, and experiment programs together, even if they are written in different languages.