Fairness in Preference-based Reinforcement Learning

Autor:	Siddique, Umer, Sinha, Abhinav, Cao, Yongcan
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computers and Society Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to The Many Facets of Preference Learning Workshop at the International Conference on Machine Learning (ICML)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.09995 Zobrazit plný text záznamu View this record from Arxiv