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

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.