Incentive-driven multi-agent reinforcement learning approach for commons dilemmas in land-use
Pelcner, Lukasz; do Carmo Alves, Matheus Aparecido; Marcolino, Leandro Soriano; Harrison, Paula 
ORCID: https://orcid.org/0000-0002-9873-3338; Atkinson, Peter.
2024
Incentive-driven multi-agent reinforcement learning approach for commons dilemmas in land-use.
In:
PRIMA 2024: principles and practice of multi-agent systems.
Cham, Switzerland, Springer Nature, 284-289.
(Lecture Notes in Computer Science, 15395).
Abstract/Summary
We propose ORAA, a novel incentive-driven algorithm that guides agents in a property-based Multi-Agent Reinforcement Learning domain to act sustainably considering a common pool of resources in an online manner. ORAA implements 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 scenario and the incentivisation dynamics. We show significant improvement in the incentives’ cost-efficiency, reducing the budget spent while increasing the collection of rewards by individual agents. Besides that, our application shows better results when using learned (approximated) models instead of using and simulating the true models of each agent for planning, saving up to 50% of the available budget for incentivisation.
| Item Type: | Publication - Book Section |
|---|---|
| Digital Object Identifier (DOI): | 10.1007/978-3-031-77367-9_21 |
| UKCEH and CEH Sections/Science Areas: | Soils and Land Use (Science Area 2017-) |
| ISBN: | 978-3-031-77367-9 |
| ISSN: | 0302-9743 |
| Additional Information. Not used in RCUK Gateway to Research.: | Manuscript version available via Related URLs 'Other' link. |
| Additional Keywords: | multi-agent systems, intelligent agents, cooperation and coordination, distributed artificial intelligence, machine learning, simulation environments, multi-agent planning, knowledge representation and reasoning |
| NORA Subject Terms: | Computer Science |
| Related URLs: | |
| Date made live: | 02 Dec 2024 09:40 +0 (UTC) |
| URI: | https://nora.nerc.ac.uk/id/eprint/538483 |
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