Fairness in Reinforcement Learning
Abstract
We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards. Our fairness constraint requires that an algorithm never prefers one action over another if the long-term (discounted) reward of choosing the latter action is higher. Our first result is negative: despite the fact that fairness is consistent with the optimal policy, any learning algorithm satisfying fairness must take time exponential in the number of states to achieve non-trivial approximation to the optimal policy. We then provide a provably fair polynomial time algorithm under an approximate notion of fairness, thus establishing an exponential gap between exact and approximate fairness
Cite
@article{arxiv.1611.03071,
title = {Fairness in Reinforcement Learning},
author = {Shahin Jabbari and Matthew Joseph and Michael Kearns and Jamie Morgenstern and Aaron Roth},
journal= {arXiv preprint arXiv:1611.03071},
year = {2017}
}
Comments
The short version of this paper appears in the proceedings of ICML-17