Governed Reward Engineering Kernels for Designing Robust Multi-Agent Reinforcement Learning Systems

The core message of this paper is to propose a framework called GOV-REK that dynamically assigns reward distributions to agents in multi-agent reinforcement learning systems during the learning stage, in order to incentivize cooperation and improve the performance of baseline reinforcement learning algorithms.