Policy iteration type algorithms for recurrent state Markov decision processes

摘要

We introduce and analyze several new policy iteration type algorithms for average cost Markov decision processes (MDPs). We limit attention to "recurrent state" processes where there exists a state which is recurrent under all stationary policies, and our analysis applies to finite-state problems with compact constraint sets, continuous transition probability functions, and lower-semicontinuous cost functions. The analysis makes use of an underlying relationship between recurrent state MDPs and the so-called stochastic shortest path problems of Bertsekas and Tsitsiklis (Math. Oper. Res. 16(3) (1991) 580). After extending this relationship, we establish the convergence of the new policy iteration type algorithms either to optimality or to within ε > 0 of the optimal average cost.