A Simultaneous Perturbation Stochastic Approximation-Based Actor–Critic Algorithm for Markov Decision Processes

Abstract

A two-timescale simulation-based actor-critic algorithm for solution of infinite horizon Markov decision processes with finite state and compact action spaces under the discounted cost criterion is proposed. The algorithm does gradient search on the slower timescale in the space of deterministic policies and uses simultaneous perturbation stochastic approximation-based estimates. On the faster scale, the value function corresponding to a given stationary policy is updated and averaged over a fixed number of epochs (for enhanced performance). The proof of convergence to a locally optimal policy is presented. Finally, numerical experiments using the proposed algorithm on flow control in a bottleneck link using a continuous time queueing model are shown.