2021/05/06 by Zizhen Zhang, Zhang, Zizhen, Zhiyuan Wu +5 · 4 citations
Computer Science · Engineering · Mathematics · #Adaptability #Advanced Multi-Objective Optimization Algorithms #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Decomposition #Engineering #FOS: Computer and information sciences #Machine learning #Mathematical optimization #Mathematics #Meta learning (computer science) #Multi-objective optimization #Reinforcement learning #Robotic Path Planning Algorithms #Travelling salesman problem #cs.AI
paper · pdf · doi:10.48550/arxiv.2105.02741
openalex publication_date 2021/05/06 · arxiv created 2022/02/13 · arxiv updated 2022/02/15 · openalex created_date 2022/08/07 · openalex updated_date 2026/07/28
Deep reinforcement learning (DRL) has recently shown its success in tackling complex combinatorial optimization problems. When these problems are extended to multiobjective ones, it becomes difficult for the existing DRL approaches to flexibly and efficiently deal with multiple subproblems determined by weight decomposition of objectives. This paper proposes a concise meta-learning-based DRL approach. It first trains a meta-model by meta-learning. The meta-model is fine-tuned with a few update steps to derive submodels for the corresponding subproblems. The Pareto front is then built accordingly. Compared with other learning-based methods, our method can greatly shorten the training time of multiple submodels. Due to the rapid and excellent adaptability of the meta-model, more submodels can be derived so as to increase the quality and diversity of the found solutions. The computational experiments on multiobjective traveling salesman problems and multiobjective vehicle routing problem with time windows demonstrate the superiority of our method over most of learning-based and iteration-based approaches.