Learning Where, What and How to Transfer: A Multi-Role Reinforcement Learning Approach for Evolutionary Multitasking

• URL: http://arxiv.org/abs/2511.15199v1
• Date: Wed, 19 Nov 2025 07:38:09 GMT
• Title: Learning Where, What and How to Transfer: A Multi-Role Reinforcement Learning Approach for Evolutionary Multitasking
• Authors: Jiajun Zhan, Zeyuan Ma, Yue-Jiao Gong, Kay Chen Tan,
• Abstract summary: We explore designing a systematic and generalizable knowledge transfer policy through Reinforcement Learning.<n>Three major challenges: determining the task to transfer (where), the knowledge to be transferred (what) and the mechanism for the transfer (how)
• Score: 32.26014625728783
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Evolutionary multitasking (EMT) algorithms typically require tailored designs for knowledge transfer, in order to assure convergence and optimality in multitask optimization. In this paper, we explore designing a systematic and generalizable knowledge transfer policy through Reinforcement Learning. We first identify three major challenges: determining the task to transfer (where), the knowledge to be transferred (what) and the mechanism for the transfer (how). To address these challenges, we formulate a multi-role RL system where three (groups of) policy networks act as specialized agents: a task routing agent incorporates an attention-based similarity recognition module to determine source-target transfer pairs via attention scores; a knowledge control agent determines the proportion of elite solutions to transfer; and a group of strategy adaptation agents control transfer strength by dynamically controlling hyper-parameters in the underlying EMT framework. Through pre-training all network modules end-to-end over an augmented multitask problem distribution, a generalizable meta-policy is obtained. Comprehensive validation experiments show state-of-the-art performance of our method against representative baselines. Further in-depth analysis not only reveals the rationale behind our proposal but also provide insightful interpretations on what the system have learned.

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