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Multi-agent Reinforcement Learning for Decentralized Stable Matching

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Algorithmic Decision Theory (ADT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13023))

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Abstract

In the real world, people/entities usually find matches independently and autonomously, such as finding jobs, partners, roommates, etc. It is possible that this search for matches starts with no initial knowledge of the environment. We propose the use of a multi-agent reinforcement learning (MARL) paradigm for a spatially formulated decentralized two-sided matching market with independent and autonomous agents. Having autonomous agents acting independently makes our environment very dynamic and uncertain. Moreover, agents lack the knowledge of preferences of other agents and have to explore the environment and interact with other agents to discover their own preferences through noisy rewards. We think such a setting better approximates the real world and we study the usefulness of our MARL approach for it. Along with conventional stable matching case where agents have strictly ordered preferences, we check the applicability of our approach for stable matching with incomplete lists and ties. We investigate our results for stability, level of instability (for unstable results), and fairness. Our MARL approach mostly yields stable and fair outcomes.

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Authors and Affiliations

  1. University of Kentucky, Lexington, KY, USA

    Kshitija Taywade, Judy Goldsmith & Brent Harrison

Authors
  1. Kshitija Taywade
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  2. Judy Goldsmith
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  3. Brent Harrison
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Corresponding author

Correspondence to Kshitija Taywade .

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Editors and Affiliations

  1. National Technical University of Athens, Athens, Greece

    Dimitris Fotakis

  2. Consejo Superior de Investigaciones Cientificas, Madrid, Madrid, Spain

    David Ríos Insua

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Taywade, K., Goldsmith, J., Harrison, B. (2021). Multi-agent Reinforcement Learning for Decentralized Stable Matching. In: Fotakis, D., Ríos Insua, D. (eds) Algorithmic Decision Theory. ADT 2021. Lecture Notes in Computer Science(), vol 13023. Springer, Cham. https://doi.org/10.1007/978-3-030-87756-9_24

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  • DOI: https://doi.org/10.1007/978-3-030-87756-9_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87755-2

  • Online ISBN: 978-3-030-87756-9

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