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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/ISSNConference contribution/Paperpeer-review

Forthcoming

Standard

Incentive-based MARL Approach for Commons Dilemmas in Property-based Environments: Extended Abstract. / Pelcner, Lukasz; do Carmo Alves, Matheus Aparecido; Soriano Marcolino, Leandro et al.
Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 23. ed. IFAAMAS, 2023.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Pelcner, L, do Carmo Alves, MA, Soriano Marcolino, L, Harrison, P & Atkinson, P 2023, Incentive-based MARL Approach for Commons Dilemmas in Property-based Environments: Extended Abstract. in Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 23 edn, IFAAMAS, The 23rd International Conference on Autonomous Agents and Multi-Agent Systems, Auckland, New Zealand, 6/05/24.

APA

Pelcner, L., do Carmo Alves, M. A., Soriano Marcolino, L., Harrison, P., & Atkinson, P. (in press). Incentive-based MARL Approach for Commons Dilemmas in Property-based Environments: Extended Abstract. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (23 ed.). IFAAMAS.

Vancouver

Pelcner L, do Carmo Alves MA, Soriano Marcolino L, Harrison P, Atkinson P. Incentive-based MARL Approach for Commons Dilemmas in Property-based Environments: Extended Abstract. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 23 ed. IFAAMAS. 2023

Author

Pelcner, Lukasz ; do Carmo Alves, Matheus Aparecido ; Soriano Marcolino, Leandro et al. / Incentive-based MARL Approach for Commons Dilemmas in Property-based Environments : Extended Abstract. Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 23. ed. IFAAMAS, 2023.

Bibtex

@inproceedings{70f941c36b0f498e856be72469a042d4,
title = "Incentive-based MARL Approach for Commons Dilemmas in Property-based Environments: Extended Abstract",
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{\textquoteright} cost-efficiency when using learned models that approximate the behaviour of each agent instead of simulating their true models.",
keywords = "Reinforcement Learning, Multi-Agents System, Property-based Environment, Common-Pool Resources",
author = "Lukasz Pelcner and {do Carmo Alves}, {Matheus Aparecido} and {Soriano Marcolino}, Leandro and Paula Harrison and Peter Atkinson",
year = "2023",
month = dec,
day = "21",
language = "English",
booktitle = "Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems",
publisher = "IFAAMAS",
edition = "23",
note = "The 23rd International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2024 ; Conference date: 06-05-2024 Through 10-05-2024",
url = "https://www.aamas2024-conference.auckland.ac.nz/",

}

RIS

TY - GEN

T1 - Incentive-based MARL Approach for Commons Dilemmas in Property-based Environments

T2 - The 23rd International Conference on Autonomous Agents and Multi-Agent Systems

AU - Pelcner, Lukasz

AU - do Carmo Alves, Matheus Aparecido

AU - Soriano Marcolino, Leandro

AU - Harrison, Paula

AU - Atkinson, Peter

N1 - Conference code: 23

PY - 2023/12/21

Y1 - 2023/12/21

N2 - 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.

AB - 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.

KW - Reinforcement Learning

KW - Multi-Agents System

KW - Property-based Environment

KW - Common-Pool Resources

M3 - Conference contribution/Paper

BT - Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

PB - IFAAMAS

Y2 - 6 May 2024 through 10 May 2024

ER -