Incentive-Driven Multi-agent Reinforcement Learning Approach for Commons Dilemmas in Land-Use

Lukasz Pelcner, Matheus Aparecido do Carmo Alves, Leandro Soriano Marcolino, Paula A. Harrison, Peter M. Atkinson

Abstract: 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.

External IDs:dblp:conf/prima/PelcnerAMHA24