Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. This text aims to provide a clear and simple account of the key ideas and algorithms of reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part one defines the reinforcement learning problems in terms of Markov decision problems. Part two provides basic solution methods - dynamic programming, Monte Carlo simulation and temporal-difference learning - and part three presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces and planning. The two final chapters present case studies and consider the future of reinforcement learning.