研究机器人团队何时该协作，基于目标函数的二阶特性判断协调时机。
When Should Agents Coordinate in Differentiable Sequential Decision Problems?
- 用目标函数的二阶性质判断协作必要性
- 提出算法识别需协调的关键时刻
- 适用于可微分运动规划问题
多机器人团队需要协作才能高效运行。若各智能体仅做个体最优决策，团队整体表现可能下降。但在许多场景中，协作需付出通信代价。本文研究一类广义的可微分运动规划问题中协作的价值。将协作行为建模为连续谱：一端是联合优化团队目标，另一端是各自主动选择最优动作（即纳什均衡）。研究发现，这类问题中的协作决策可归结为对智能体目标函数二阶性质的分析，并据此设计算法，确定团队应在哪些时刻进行协调。
原文摘要 · Abstract (English)
Multi-robot teams must coordinate to operate effectively. When a team operates in an uncoordinated manner, and agents choose actions that are only individually optimal, the team's outcome can suffer. However, in many domains, coordination requires costly communication. We explore the value of coordination in a broad class of differentiable motion-planning problems. In particular, we model coordinated behavior as a spectrum: at one extreme, agents jointly optimize a common team objective, and at the other, agents make unilaterally optimal decisions given their individual decision variables, i.e., they operate at Nash equilibria. We then demonstrate that reasoning about coordination in differentiable motion-planning problems reduces to reasoning about the second-order properties of agents' objectives, and we provide algorithms that use this second-order reasoning to determine at which times a team of agents should coordinate.
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