标题： 基于有限状态控制器的确定性POMDP离线求解器 显示英文标题

标题： A Finite-State Controller Based Offline Solver for Deterministic POMDPs

摘要： 确定性部分可观察马尔可夫决策过程（DetPOMDPs）经常出现在规划问题中，其中智能体对其环境状态不确定，但可以确定地执行动作和进行观测。 本文提出了一种针对DetPOMDPs的Monte Carlo值迭代（MCVI）算法的改进版本DetMCVI，该方法以有限状态控制器（FSCs）的形式构建策略。 DetMCVI以高成功率解决了大规模问题，优于现有的DetPOMDPs基准方法。 我们还在现实世界中的移动机器人森林测绘场景中验证了该算法的性能。 显示英文摘要

摘要： Deterministic partially observable Markov decision processes (DetPOMDPs) often arise in planning problems where the agent is uncertain about its environmental state but can act and observe deterministically. In this paper, we propose DetMCVI, an adaptation of the Monte Carlo Value Iteration (MCVI) algorithm for DetPOMDPs, which builds policies in the form of finite-state controllers (FSCs). DetMCVI solves large problems with a high success rate, outperforming existing baselines for DetPOMDPs. We also verify the performance of the algorithm in a real-world mobile robot forest mapping scenario.