MAPG 2 : Multiagent Policy Gradient via Potential Game for Multirobot Task Allocation Problems

Le résumé fourni par la source

Efficient task allocation among multiple UAVs and autonomous robots is critical in modern IoT scenarios. This is typically modeled as a multi-robot task allocation (MRTA) problem, known to be an NP-hard combinatorial optimization problem. Neural sequential modeling combined with reinforcement learning (RL) optimization has emerged as a promising paradigm for solving this problem, owing to its high efficiency during inference. However, most existing methods assume that each robot is capable of performing only a single type of task. The development of sensing technologies has significantly enhanced the functional diversity of robots, thereby challenging the effectiveness and scalability of traditional methods. This paper considers a variant of the MRTA problem, where each robot is capable of handling multiple tasks, and tasks vary in both their types and required resources. To this end, we present a novel game-theoretic multi-agent RL algorithm called multi-agent policy gradient via potential game (MAPG2). The key components of proposed method consist of three parts. Firstly, we utilize graph-based attention model (GAM) to characterize the representations between tasks. Secondly, we formulate the single-step allocation process as a potential game (PG) to guarantee the consistency and soundness of the reward function design. Lastly, our approach sequentially generates allocation strategies through centralized training and decentralized execution (CTDE) framework. Extensive experiments demonstrate that MAPG2achieves a 10% improvement in task completion rate compared to state-of-the-art baselines, validating its effectiveness and robustness.

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