Using DCOPs to Balance Exploration and Exploitation in Time-Critical Domains

Substantial work has investigated balancing exploration and exploitation, but relatively little has addressed this tradeoff in the context of coordinated multi-agent interactions. This paper introduces a class of problems in which agents must maximize their on-line reward, a decomposable function dependent on pairs of agent’s decisions. Unlike previous work, agents must both learn the reward function and exploit it on-line, critical properties for a class of physicallymotivated systems, such as mobile wireless networks. This paper introduces algorithms motivated by the Distributed Constraint Optimization Problem framework and demonstrates when, and at what cost, increasing agents’ coordination can improve the global reward on such problems.