Percentile performance criteria for limiting average Markovdecision processes

摘要

Addresses the following basic feasibility problem for infinite-horizon Markov decision processes (MDPs): can a policy be found that achieves a specified value (target) of the long-run limiting average reward at a specified probability level (percentile)? Related optimization problems of maximizing the target for a specified percentile and vice versa are also considered. The authors present a complete (and discrete) classification of both the maximal achievable target levels and of their corresponding percentiles. The authors also provide an algorithm for computing a deterministic policy corresponding to any feasible target-percentile pair. Next the authors consider similar problems for an MDP with multiple rewards and/or constraints. This case presents some difficulties and leads to several open problems. An LP-based formulation provides constructive solutions for most cases