针对多智能体规划难题，提出高效求解确定性动态模型的新方法。
Scalable Solution Methods for Dec-POMDPs with Deterministic Dynamics
- 基于联合策略均衡搜索框架，设计专用求解器IDPP
- 可处理现有方法无法高效解决的大规模问题
- 适合需要精确协同决策的机器人路径规划场景
许多高层多智能体规划问题，如多机器人导航与路径规划，可通过确定性动作和观测进行有效建模。本文聚焦此类领域，引入确定性分布式部分可观测马尔可夫决策过程（Det-Dec-POMDPs）这一新类别，其特征为状态转移和观测在给定状态与联合动作下是确定性的。为此，我们提出一种实用求解器——迭代确定性POMDP规划（IDPP）。该方法基于经典的联合策略均衡搜索框架，并专门优化以应对当前Dec-POMDP求解器难以高效处理的大规模Det-Dec-POMDP问题。
原文摘要 · Abstract (English)
Many high-level multi-agent planning problems, including multi-robot navigation and path planning, can be effectively modeled using deterministic actions and observations. In this work, we focus on such domains and introduce the class of Deterministic Decentralized POMDPs (Det-Dec-POMDPs). This is a subclass of Dec-POMDPs characterized by deterministic transitions and observations conditioned on the state and joint actions. We then propose a practical solver called Iterative Deterministic POMDP Planning (IDPP). This method builds on the classic Joint Equilibrium Search for Policies framework and is specifically optimized to handle large-scale Det-Dec-POMDPs that current Dec-POMDP solvers are unable to address efficiently.
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