Reinforcement Learning in Multi-Objective Multi-Agent Systems: Doctoral Consortium

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For effective decision-making in the real world, artificial agents need to take both the multi-agent as well as multi-objective nature of their environments into account. These environments are formalised as multi-objective games and introduce numerous challenges compared to their single-objective counterpart. For my main contributions so far, I have established a theoretical guarantee that a bidirectional link always exists that maps a finite multi-objective game to an equivalent single-objective game with an infinite number of actions. Additionally, I presented an extensive study of Nash equilibria in multi-objective games, culminating in existence guarantees under certain assumptions. From a reinforcement learning perspective, I explored how communication and commitment can help agents to learn adequate policies in these challenging environments. In this paper, I summarise my ongoing research and discuss several promising directions for future work.
Original languageEnglish
Title of host publicationThe 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Publication statusAccepted/In press - 8 Mar 2023


  • Multi-objective
  • Game theory
  • Nash equilibrium

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