Proceedings

Multi-Objective Reinforcement Learning for Designing Ethical Environments

Manel Rodriguez-Soto, Maite Lopez-Sanchez, Juan A. Rodriguez Aguilar

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 545-551.

https://doi.org/10.24963/ijcai.2021/76

Keywords

  • AI Ethics, Trust, Fairness: Moral Decision Making
  • Machine Learning: Reinforcement Learning

Abstract

AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. A common approach, founded on the exploitation of Reinforcement Learning techniques, is to design environments that incentivise agents to behave ethically. However, to the best of our knowledge, current approaches do not theoretically guarantee that an agent will learn to behave ethically. Here, we make headway along this direction by proposing a novel way of designing environments wherein it is formally guaranteed that an agent learns to behave ethically while pursuing its individual objectives. Our theoretical results develop within the formal framework of Multi-Objective Reinforcement Learning to ease the handling of an agent's individual and ethical objectives. As a further contribution, we leverage on our theoretical results to introduce an algorithm that automates the design of ethical environments.

Manel Rodriguez-Soto, Maite Lopez-Sanchez, and Juan A. Rodriguez Aguilar. 2021. Multi-Objective Reinforcement Learning for Designing Ethical Environments. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 545–551. https://doi.org/10.24963/ijcai.2021/76
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