Using Case-Based Reasoning as a Reinforcement Learning Framework for Optimizationwith Changing Criteria

摘要

Practical optimization problems such as job-shop scheduling of-ten involveoptimization criteria that change over time. Repair-based frameworks have been identified as flexible computational paradigms for difficult combinatorial optimization problems. Since the control problem of repair-based optimization is severe, Reinforcement Learning (RL) techniques can be potentially helpful. However, some of the fundamental assumptions made by traditional RL algorithms are not valid for repair-based optimization. Case-Based Reasoning (CBR) compensates for some of the limitations of traditional RL approaches. In this paper, we present a Case-Based Reasoning RL approach, implemented in the CABINS system, for repair-based optimization. We chose job-shop scheduling as the testbed for our approach. Our experimental results show that CABINS is able to effectively solve problems with changing optimization criteria which are not known to the system and only exist implicitly in a extensional manner in the case base.