  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.
