This chapter combines the methods from the previous two chapters, namely, function approximation and simulation. It introduces policy-centric methods that are based on approximating the action-choice probability distribution of a randomized policy. Value function approximation using Monte Carlo and temporal differencing methods is described. Optimization algorithms classified as value-based algorithms (Q-learning and Q-policy iteration) and policy-based algorithms (policy gradient and actor-critic) are presented. The chapter concludes with brief remarks on additional topics, including simultaneous learning and control, experience replay, deep learning, importance sampling, Monte Carlo tree search, and partially observable models.