Strategy and Fairness in Repeated Two-agent Interaction

Abstract

The criterion of fairness has not been given much attention in the research of multi-agent learning problem. We propose an adaptive strategy for agents to achieve fairness in repeated two-agent game with conflicting interests. In our strategy, each agent is equipped with inequity-averse based fairness model, and makes its decision according to its attractiveness for each action. Besides, each agent adjusts its own attitudes in an adaptive way on the basis of previous outcome and the payoff distribution of the agents in the system, and our goal is to reach fairness in the sense of obtaining equal accumulated payoffs for each agent. Simulation results show that agents using our strategy can coordinate well with each other and achieve fairness with less payoff cost than previous work.

Learning to Achieve Socially Optimal Solutions in General-Sum Games

“…As future work, we are going to further explore how to utilize the characteristics of the opponents' strategies towards the targeted goal when interacting with non bestresponse learners. Besides, we are also interested in investigating how to handel the cases when the targeted solution consists of a sequence of joint actions such as achieving fairness [7].…”