Learning to Coordinate with Coordination Graphs in Repeated SingleStage MultiAgent Decision Problems
[edit]
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:482490, 2018.
Abstract
Learning to coordinate between multiple agents is an important problem in many reinforcement learning problems. Key to learning to coordinate is exploiting loose couplings, i.e., conditional independences between agents. In this paper we study learning in repeated fully cooperative games, multiagent multiarmed bandits (MAMABs), in which the expected rewards can be expressed as a coordination graph. We propose multiagent upper confidence exploration (MAUCE), a new algorithm for MAMABs that exploits loose couplings, which enables us to prove a regret bound that is logarithmic in the number of arm pulls and only linear in the number of agents. We empirically compare MAUCE to sparse cooperative Qlearning, and a stateoftheart combinatorial bandit approach, and show that it performs much better on a variety of settings, including learning control policies for wind farms.
Related Material


