  In this paper, we address the issue of fairness in preference-based
reinforcement learning (PbRL) in the presence of multiple objectives. The main
objective is to design control policies that can optimize multiple objectives
while treating each objective fairly. Toward this objective, we design a new
fairness-induced preference-based reinforcement learning or FPbRL. The main
idea of FPbRL is to learn vector reward functions associated with multiple
objectives via new welfare-based preferences rather than reward-based
preference in PbRL, coupled with policy learning via maximizing a generalized
Gini welfare function. Finally, we provide experiment studies on three
different environments to show that the proposed FPbRL approach can achieve
both efficiency and equity for learning effective and fair policies.

Machine Learning;Artificial Intelligence;Computers and Society;Systems and Control;Systems and Control