Multi-AGV motion planning based on deep reinforcement learning
To solve the problem of multi-Automated Guided Vehicle(AGV)conflict-free motion planning in mobile robot fulfillment systems,a Markov Decision Process(MDP)model was constructed,then a novel planning ap-proach based on Deep Q-Network(DQN)was proposed.With AGVs'positions as inputs,the DQN was trained by using classical deep Q-learning algorithm and was used to estimate the maximum expected cumulative reward re-ceived from taking each action.Computational results of problem instances showed that the proposed approach could effectively overcome the potential collisions of AGV fleet in motion,and thus enabled the AGV fleet to accomplish all rack transportation tasks with conflict-free.Furthermore,compared to an existing planning heuristic in the liter-ature,the motion plans of AGVs generated from the proposed approach requid shorter average makespans.
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