A Survey of solution techniques for the partially observed Markov decision process <s> ?(ei,a',u)+-~ ~f)i)(a')v(e)), ) <s> This paper proposes bounds and action elimination procedures for policy iteration and modified policy iteration. Procedures to eliminate nonoptimal actions for one iteration and for all subsequent iterations are presented. The implementation of these procedures is discussed and encouraging computational results are presented. <s> BIB001 </s> A Survey of solution techniques for the partially observed Markov decision process <s> ?(ei,a',u)+-~ ~f)i)(a')v(e)), ) <s> We present three algorithms to solve the infinite horizon, expected discounted total reward partially observed Markov decision process POMDP. Each algorithm integrates a successive approximations algorithm for the POMDP due to A. Smallwood and E. Sondik with an appropriately generalized numerical technique that has been shown to reduce CPU time until convergence for the completely observed case. The first technique is reward revision. The second technique is reward revision integrated with modified policy iteration. The third is a standard extrapolation. A numerical study indicates the potentially significant computational value of these algorithms. <s> BIB002
where a' = 6(u)(ei) and where 6(u) ~ A is such that H~(~)u = Hu. Observe that the first operator requires HA times as many operations per iteration as does the second operator. Thus, approach 2, which combines reward revision and a modified policy iteration BIB001  appears to be more numerically attractive per iteration than does approach 1. This conjecture has been supported by numerical testing. Theoretical results associated with approach 2 are not nearly as well developed as those for approach 1; see BIB002 for details. 