A Deep Reinforcement Learning-Assisted Multimodal Multiobjective Bilevel Optimization Method for Multirobot Task Allocation

Abstract

Multirobot task allocation (MRTA) is a challenging bi-level problem in the multirobot cooperative systems (MRCSs) and offers an effective method for addressing complex tasks. However, dynamic /uncertain environments can easily invalidate original schemes in practical MRTA decision-makings. Further, a nested structure in MRTA problems makes computational expensive. Therefore, the two main tasks are 1) finding a sufficient number of equivalent schemes for MRTA problems to adapt to task environments and 2) improving algorithm search efficiency in bi-level optimization problems. In this study, a multimodal multiobjective evolutionary algorithm (MMOEA) based on deep reinforcement learning (DRL) and large neighborhood search (LNS), called MMOEA-DL, is proposed to solve MRTA problems. In the MMOEA-DL, the task allocation problem, which is considered as the upper-level optimization problem, is solved using an improved MMOEA. The traveling salesman problem (TSP) regarded as the lower-level optimization problem is addressed via end-to-end method (i.e., DRL) and LNS. By leveraging the end-to-end method to obtain the results of the lower-level optimization, the bi-level optimization problem is effectively transformed into a single-level optimization problem. To demonstrate the performance of the proposed algorithm, 16 MRTA simulation scenarios and two actual MRTA scenarios with evenly and unevenly distributed task points are introduced in the present study. The simulation results verify that the MMOEA-DL not only provides decision-makers with expanded equivalent optimal schemes to address dynamic environments or unforeseen circumstances, but also offers a novel approach to solve the multimodal multiobjective bi-level optimization problem while saving computational costs.