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

Understand

In multiagent systems, the complex interaction of fixed incentives can lead agents to outcomes that are poor (inefficient) not only for the group, but also for each individual.

• Price of anarchy is a technical, game-theoretic definition that quantifies the inefficiency arising in these scenarios -- it compares the welfare that can be achieved through perfect coordination against that achieved by self-interested agents at a Nash equilibrium.
• We derive a differentiable, upper bound on a price of anarchy that agents can cheaply estimate during learning.
• Equipped with this estimator, agents can adjust their incentives in a way that improves the efficiency incurred at a Nash equilibrium.