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Talk
Fairness in Reinforcement Learning
Shahin Jabbari · Matthew Joseph · Michael Kearns · Jamie Morgenstern · Aaron Roth

Sun Aug 06 06:06 PM -- 06:24 PM (PDT) @ C4.5
in Reinforcement learning 1 »

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.

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Author Information

Shahin Jabbari (University of Pennsylvania)
Matthew Joseph (University of Pennsylvania)
Michael Kearns (University of Pennsylvania)
Jamie Morgenstern (University of Pennsylvania)
Aaron Roth (University of Pennsylvania)

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