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Updates every hour. Last Updated: 31-Mar-2026 22:15 ET (1-Apr-2026 02:15 GMT/UTC)
A graph reinforcement learning framework for real-time distributed multi-robot task allocation
Shanghai Jiao Tong University Journal CenterDynamic multi-robot task allocation (MRTA) requires real-time responsiveness and adaptability to rapidly changing con ditions. Existing methods, primarily based on static data and centralized architectures, often fail in dynamic environments that require decentralized, context-aware decisions. To address these challenges, this paper proposes a novel graph reinforce ment learning (GRL) architecture, named Spatial-Temporal Fusing Reinforcement Learning (STFRL), to address real-time distributed target allocation problems in search and rescue scenarios. The proposed policy network includes an encoder, which employs a Temporal-Spatial Fusing Encoder (TSFE) to extract input features and a decoder uses multi-head attention (MHA) to perform distributed allocation based on the encoder’s output and context. The policy network is trained with the REINFORCEalgorithm.Experimentalcomparisonswithstate-of-the-artbaselinesdemonstratethatSTFRLachievessuperior performance in path cost, inference speed, and scalability, highlighting its robustness and efficiency in complex, dynamic environments.
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