Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning
AbstractA large class of problems of sequential decision making under uncertainty, of which the underlying probability structure is a Markov process, can be modeled as stochastic dynamic programs (referred to, in general, as Markov decision problems or MDPs). However, the computational complexity of the classical MDP algorithms, such as value iteration and policy iteration, is prohibitive and can grow intractably with the size of the problem and its related data. Furthermore, these techniques require for each action the one step transition probability and reward matrices, and obtaining these is often unrealistic for large and complex systems. Recently, there has been much interest in a simulation-based stochastic approximation framework called reinforcement learning (RL), for computing near optimal policies for MDPs. RL has been successfully applied to very large problems, such as elevator scheduling, and dynamic channel allocation of cellular telephone systems. In this paper, we extend RL to a more general class of decision tasks that are referred to as semi-Markov decision problems (SMDPs). In particular, we focus on SMDPs under the average-reward criterion. We present a new model-free RL algorithm called SMART (Semi-Markov Average Reward Technique). We present a detailed study of this algorithm on a combinatorially large problem of determining the optimal preventive maintenance schedule of a production inventory system. Numerical results from both the theoretical model and the RL algorithm are presented and compared.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by INFORMS in its journal Management Science.
Volume (Year): 45 (1999)
Issue (Month): 4 (April)
semi-Markov decision processes (SMDP); reinforcement learning; average reward; preventive maintenance;
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Giannoccaro, Ilaria & Pontrandolfo, Pierpaolo, 2002. "Inventory management in supply chains: a reinforcement learning approach," International Journal of Production Economics, Elsevier, vol. 78(2), pages 153-161, July.
- Schütz, Hans-Jörg & Kolisch, Rainer, 2012. "Approximate dynamic programming for capacity allocation in the service industry," European Journal of Operational Research, Elsevier, vol. 218(1), pages 239-250.
- Ohno, Katsuhisa, 2011. "The optimal control of just-in-time-based production and distribution systems and performance comparisons with optimized pull systems," European Journal of Operational Research, Elsevier, vol. 213(1), pages 124-133, August.
- van Wezel, M.C. & van Eck, N.J.P., 2005. "Reinforcement learning and its application to Othello," Econometric Institute Research Papers EI 2005-47, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Li, Xueping & Wang, Jiao & Sawhney, Rapinder, 2012. "Reinforcement learning for joint pricing, lead-time and scheduling decisions in make-to-order systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 99-109.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc).
If references are entirely missing, you can add them using this form.