Reinforcement Learning Approach for Adaptive Negotiation-Rules Acquisition in AGV Transportation Systems

Abstract

In this paper, we introduce an autonomous decentralized method for directing multiple automated guided vehicles (AGVs) in response to uncertain delivery requests. The transportation route plans of AGVs are expected to minimize the transportation time while preventing collisions between the AGVs in the system. In this method, each AGV as an agent computes its transportation route by referring to the static path information. If potential collisions are detected, one of the two agents chosen by a negotiation-rule modifies its route plan. Here, we propose a reinforcement learning approach for improving the negotiation-rules. Then, we confirm the effectiveness of the proposed approach based on the results of computational experiments.