Reinforcement Learning with Stepwise Fairness Constraints

Zhun Deng, He Sun, Zhiwei Steven Wu, Linjun Zhang, David C. Parkes

Submitted on 7 November 2022


AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making. Moreover, many settings are dynamic, with populations responding to sequential decision policies. We introduce the study of reinforcement learning (RL) with stepwise fairness constraints, requiring group fairness at each time step. Our focus is on tabular episodic RL, and we provide learning algorithms with strong theoretical guarantees in regard to policy optimality and fairness violation. Our framework provides useful tools to study the impact of fairness constraints in sequential settings and brings up new challenges in RL.


Comment: Fairness, Reinforcement Learning

Subjects: Computer Science - Machine Learning; Computer Science - Artificial Intelligence; Computer Science - Computers and Society