A Review of Reinforcement Learning

There's a great new book on the market that lays out the conceptual and algorithmic foundations of this exciting area. Reinforcement learning pioneers Rich Sutton and Andy Barto have published Reinforcement Learning: An Introduction, providing a highly accessible starting point for interested students, researchers, and practitioners. In the reinforcement learning framework, an agent acts in an environment whose state it can sense and occasionally receives some penalty or reward based on its state and action. Its learning task is to find a policy for action selection that maximizes its reward over the long haul; this task requires not only choosing actions that are associated with high reward in the current state but thinking ahead by choosing actions that will lead the agents to more lucrative parts of the state space. Although there are many ways to attack this problem, the paradigm described in the book is to construct a value function that evaluates the "goodness" of different situations.