Fairness in Cooperative Multi-agent Multi-objective Reinforcement Learning using the Expected Scalarized Return

Fairness is essential for deploying artificial decision-making agents in the real world. Existing work in sequential decision-making ensures fairness among agents or objectives but struggles with real-world problems that are both multi-agent and multi-objective. Furthermore, research integrating fairness into Multi-Objective Reinforcement Learning (MORL) is focused on ensuring fairness over the objectives only on the average of several executions of a policy, which is achived by optimizing the policy's scalarized expected return (SER). To achieve fairness over objectives during each execution the expected scalarized return (ESR) of a policy needs to be optimized instead. This paper presents an argument on the necessity of using ESR in the context of fair multi-objective decision-making and proposes the first mono-policy algorithm able to learn efficient decentralized policies while ensuring fairness across objectives under ESR.