Multiagent Dynamic Task Allocation Based on Graph Neural Reinforcement Learning Algorithm
TL;DR
A spatiotemporal topology-aware graph reinforcement learning (STA-GRL) framework that integrates a spatiotemporal gated graph attention (STGGA) module that employs a temporal gating mechanism to dynamically prioritize tasks with rapidly decaying deadlines to address multi-agent dynamic task allocation limitations.
Abstract
Abstract Multi-agent dynamic task allocation (MADTA) for UAV swarm and autonomous systems remains a formidable challenge in highly uncertain and stochastic environments, where conventional reinforcement learning methods struggle with variable input dimensions and coordination conflicts. This paper proposes a spatiotemporal topology-aware graph reinforcement learning (STA-GRL) framework to address these limitations. By modeling the environment as a dynamic bipartite graph, the framework integrates a spatiotemporal gated graph attention (STGGA) module that employs a temporal gating mechanism to dynamically prioritize tasks with rapidly decaying deadlines. A topology-aware critic is further designed to penalize spatial conflicts among agents via an enhanced adjacency matrix. Extensive simulations demonstrate that STA-GRL significantly surpasses state-of-the-art baselines. In the primary evaluation scenario with 30 agents and an intermediate task arrival rate (λ = 0.6), STA-GRL achieves a task completion rate of 86.8% and an average response time of 18.4 seconds, while reducing the average conflict rate to just 2.1%. Moreover, ablation studies confirm the critical contribution of each architectural component, with the temporal gate improving the completion rate by 7.3% and the topology-aware critic reducing conflicts by 6.4%.