Corpus ID: 221081510

Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards

@inproceedings{Siddique2020LearningFP,
  title={Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards},
  author={Umer Siddique and Paul Weng and Matthieu Zimmer},
  booktitle={ICML},
  year={2020}
}
As the operations of autonomous systems generally affect simultaneously several users, it is crucial that their designs account for fairness considerations. In contrast to standard (deep) reinforcement learning (RL), we investigate the problem of learning a policy that treats its users equitably. In this paper, we formulate this novel RL problem, in which an objective function, which encodes a notion of fairness that we formally define, is optimized. For this problem, we provide a theoretical… Expand
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