D3C: Reducing the Price of Anarchy in Multi-Agent Learning

10/01/2020
by   Ian Gemp, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset