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

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Samenvatting

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.
Originele taal-2English
TitelThe 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)
UitgeverijInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
ISBN van elektronische versie9781450394321
StatusAccepted/In press - 8 mrt 2023
EvenementThe 22nd International Conference on Autonomous Agents and Multiagent Systems - London, United Kingdom
Duur: 29 mei 20232 jun 2023
https://aamas2023.soton.ac.uk

Conference

ConferenceThe 22nd International Conference on Autonomous Agents and Multiagent Systems
Verkorte titelAAMAS 2023
Land/RegioUnited Kingdom
StadLondon
Periode29/05/232/06/23
Internet adres

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