具有长期平均目标的POMDP的记忆策略
Finite-Memory Strategies in POMDPs with Long-Run Average Objectives
摘要：
部分可观测马尔可夫决策过程（POMDPs）是不确定环境中具有概率和不确定行为的动态系统的标准模型。我们证明了在长期平均目标的POMDPs中，决策者具有有限记忆的近似最优策略。这意味着，特别是，近似长期值是递归的，以及一个弱连续性的值相对于过渡函数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
