Finite-Memory Strategies in POMDPs with Long-Run Average Objectives
证明了在部分可观测马尔可夫决策过程中，对于长期平均目标，决策者存在近似最优的有限记忆策略，这意味着长期价值的近似是可递归枚举的。
Partially observable Markov decision processes (POMDPs) are standard models for dynamic systems with probabilistic and nondeterministic behaviour in uncertain environments. We prove that in POMDPs with long-run average objective, the decision maker has approximately optimal strategies with finite memory. This implies notably that approximating the long-run value is recursively enumerable, as well as a weak continuity property of the value with respect to the transition function.