Using Case-Based Reasoning as a Reinforcement Learning framework for Optimization with Changing Criteria

Practical optimization problems such as job-shop scheduling often involve optimization criteria that change over time. Repair-based frameworks have been identi ed as exible computational paradigms for di cult 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 e ectively 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.