An introduction to dynamic programming and reinforcement learning

This chapter introduces dynamic programming and reinforcement learning techniques, and the formal model behind the problem they solve: the Markov decision process. Deterministic and stochastic Markov decision processes are discussed in turn, and their optimal solution is characterized. Three categories of dynamic programming and reinforcement learning algorithms are described: value iteration, policy iteration, and policy search.