Learning to Cooperate: Application of Deep Reinforcement Learning for Online AGV Path Finding
Multi agent path finding (MAPF) naturally exists in applications like picking up and dropping off parcels by automated guided vehicles (AGVs) in the warehouse. Existing algorithms, like conflictbased search (CBS) windowed hierarchical cooperative A* (WHCA) and other A* variants, are widely used to find the shortest paths in different manners. However, in real world environments, MAPF cases are dynamically generated and need to be solved in real time. In this work, a decentralized multi agent reinforcement learning (MARL) framework with multi step ahead tree search (MATS) strategy is proposed to make efficient decisions. Through performing experiments on a 30 x 30 grid world and a real world warehouse case, our proposed MARL policy is proved to be capable of: 1) scaling to a large number of agents in real world environment with online response time within acceptable levels; 2) outperforming existing algorithms with shorter path length and solution time, as the number of agents increases.
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