Pareto-Optimal Coverage Control for Multi-Objective Multi-Agent Systems: A General Sum Game Reinforcement Learning Perspective

Individual performance variations, target conflicts, and dynamic scenarios pose challenges in creating effective solutions to balance individual and collective benefits in collaborative multi-agent area coverage control. To address these challenges, this paper proposes a semi-distributed Pareto-optimal policy iteration algorithm (SDPOPI). This algorithm introduces a multi-objective group game (MOGG) model that accounts for conflicting objectives such as coverage efficiency, group density, energy consumption, and communication topology equilibrium. By guiding the distributed reinforcement learning policy iteration process through a game interaction mechanism, the algorithm establishes a dual-layer semi-distributed policy iteration frame-work that achieves the Pareto-Nash equilibrium for the MOGG, thereby balancing individual and group benefits. Simulation results validate the accuracy and practical applicability of the proposed method.