A variety of cooperative multi-agent control problems require agents to
achieve individual goals while contributing to collective success. This
multi-goal multi-agent setting poses difficulties for recent algorithms, which
primarily target settings with a single global reward, due to two new
challenges: efficient exploration for learning both individual goal attainment
and cooperation for others' success, and credit-assignment for interactions
between actions and goals of different agents. To address both challenges, we
restructure the problem into a novel two-stage curriculum, in which
single-agent goal attainment is learned prior to learning multi-agent
cooperation, and we derive a new multi-goal multi-agent policy gradient with a
credit function for localized credit assignment. We use a function augmentation
scheme to bridge value and policy functions across the curriculum. The complete
architecture, called CM3, learns significantly faster than direct adaptations
of existing algorithms on three challenging multi-goal multi-agent problems:
cooperative navigation in difficult formations, negotiating multi-vehicle lane
changes in the SUMO traffic simulator, and strategic cooperation in a Checkers
environment.

本文提出了一种名为 CM3 的结构，包括单个代理目标达成先于多个代理协作的学习过程和一种新的多目标多代理政策梯度，具有局部信任分配的信用功能。该结构在解决多个多目标多代理问题方面具有明显优势。