D3C: Reducing the price of anarchy in multi-agent learning
Ian Gemp, Kevin R. McKee, Richard Everett, Edgar A. Duéñez-Guzmán, Yoram Bachrach, David Balduzzi, & Andrea Tacchetti
Abstract
Even in simple multi-agent systems, fixed incentives can lead to outcomes that are poor for the group and each individual agent. We propose a method, D3C, for online adjustment of agent incentives that reduces the loss incurred at a Nash equilibrium. Agents adjust their incentives by learning to mix their incentive with that of other agents, until a compromise is reached in a distributed fashion. We show that D3C improves outcomes for each agent and the group as a whole on several social dilemmas including a traffic network with Braess's paradox, a prisoner's dilemma, and several reinforcement learning domains.
Venue
Proc. of the 21st International Conference on Autonomous Agents and MultiAgent Systems
Year
2022
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