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MAPG2: Multiagent Policy Gradient via Potential Game for Multirobot Task Allocation Problems
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AI
DOI：10.1109/JIOT.2025.3638954.png)
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Abstract
En 中文
Efficient task allocation among multiple UAVs and autonomous robots is critical in modern Internet of Things (IoT) scenarios. This is typically modeled as a multirobot task allocation (MRTA) problem, known to be an NP-hard combinatorial optimization (CO) 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 article 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 multiagent RL algorithm called multiagent policy gradient via potential game (MAPG2). The key components of the proposed method consist of three parts. First, we utilize a graph-based attention model (GAM) to characterize the representations between tasks. Second, we formulate the single-step allocation process as a potential game (PG) to guarantee the consistency and soundness of the reward function design. Finally, our approach sequentially generates allocation strategies through a centralized training and decentralized execution (CTDE) framework. Extensive experiments demonstrate that MAPG2 achieves a 10% improvement in task completion rate compared to state-of-the-art baselines, validating its effectiveness and robustness.
Keywords:
Attention model
autonomous Internet of Things (IoT) robots
multiagent policy gradient
multirobot task allocation (MRTA)
potential game (PG)
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