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In many domains such as transportation and logistics, search and rescue, orcooperative surveillance, tasks are pending to be allocated with theconsideration of possible execution uncertainties. Existing task coordinationalgorithms either ignore the stochastic process or suffer from thecomputational intensity. Taking advantage of the weakly coupled feature of theproblem and the opportunity for coordination in advance, we propose adecentralized auction-based coordination strategy using a newly formulatedscore function which is generated by forming the problem into task-constrainedMarkov decision processes (MDPs). The proposed method guarantees convergenceand at least 50% optimality in the premise of a submodular reward function.Furthermore, for the implementation on large-scale applications, an approximatevariant of the proposed method, namely Deep Auction, is also suggested with theuse of neural networks, which is evasive of the troublesome for constructingMDPs. Inspired by the well-known actor-critic architecture, two Transformersare used to map observations to action probabilities and cumulative rewardsrespectively. Finally, we demonstrate the performance of the two proposedapproaches in the context of drone deliveries, where the stochastic planningfor the drone league is cast into a stochastic price-collecting Vehicle RoutingProblem (VRP) with time windows. Simulation results are compared withstate-of-the-art methods in terms of solution quality, planning efficiency andscalability.

An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards