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  • pelcner2024incentive

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Incentive-based MARL Approach for Commons Dilemmas in Property-based Environments: Extended Abstract

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Forthcoming
Publication date21/12/2023
Host publicationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems
PublisherIFAAMAS
Number of pages3
Edition23
<mark>Original language</mark>English
EventThe 23rd International Conference on Autonomous Agents and Multi-Agent Systems - Auckland, New Zealand
Duration: 6/05/2024 → 10/05/2024
Conference number: 23
https://www.aamas2024-conference.auckland.ac.nz/

Conference

ConferenceThe 23rd International Conference on Autonomous Agents and Multi-Agent Systems
Abbreviated titleAAMAS 2024
Country/TerritoryNew Zealand
CityAuckland
Period6/05/24 → 10/05/24
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Conference

ConferenceThe 23rd International Conference on Autonomous Agents and Multi-Agent Systems
Abbreviated titleAAMAS 2024
Country/TerritoryNew Zealand
CityAuckland
Period6/05/24 → 10/05/24
Internet address

Abstract

We propose ORAA, a novel online incentive algorithm that guides agents in a property-based MARL domain to act sustainably with a common pool of resources. ORAA uses our proposed P-MADDPG model to learn and make decisions over the decentralised agents. We test our solutions in our novel domain, the ``Pollinators' Game'', which simulates a property-based MARL scenario and its incentivisation dynamics. We show significant improvement in the incentives’ cost-efficiency when using learned models that approximate the behaviour of each agent instead of simulating their true models.