Deep Coordination Graphs

Wendelin Boehmer · Vitaly Kurin · Shimon Whiteson

Keywords: [ Deep Reinforcement Learning ] [ Multiagent Learning ] [ Reinforcement Learning - Deep RL ]

[ Abstract ]
Wed 15 Jul noon PDT — 12:45 p.m. PDT
Thu 16 Jul 1 a.m. PDT — 1:45 a.m. PDT


This paper introduces the deep coordination graph (DCG) for collaborative multi-agent reinforcement learning. DCG strikes a flexible trade-off between representational capacity and generalization by factoring the joint value function of all agents according to a coordination graph into payoffs between pairs of agents. The value can be maximized by local message passing along the graph, which allows training of the value function end-to-end with Q-learning. Payoff functions are approximated with deep neural networks that employ parameter sharing and low-rank approximations to significantly improve sample efficiency. We show that DCG can solve predator-prey tasks that highlight the relative overgeneralization pathology, as well as challenging StarCraft II micromanagement tasks.

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