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Automated Guided Vehicles (AGVs) have been widely used for material handlingin flexible shop floors. Each product requires various raw materials tocomplete the assembly in production process. AGVs are used to realize theautomatic handling of raw materials in different locations. Efficient AGVs taskallocation strategy can reduce transportation costs and improve distributionefficiency. However, the traditional centralized approaches make high demandson the control center's computing power and real-time capability. In thispaper, we present decentralized solutions to achieve flexible andself-organized AGVs task allocation. In particular, we propose two improvedmulti-agent reinforcement learning algorithms, MADDPG-IPF (InformationPotential Field) and BiCNet-IPF, to realize the coordination among AGVsadapting to different scenarios. To address the reward-sparsity issue, wepropose a reward shaping strategy based on information potential field, whichprovides stepwise rewards and implicitly guides the AGVs to different materialtargets. We conduct experiments under different settings (3 AGVs and 6 AGVs),and the experiment results indicate that, compared with baseline methods, ourwork obtains up to 47\% task response improvement and 22\% training iterationsreduction.

Decentralized Multi-AGV Task Allocation based on Multi-Agent Reinforcement Learning with Information Potential Field Rewards