Persistently Optimal Policies in Stochastic Dynamic Programming with Generalized Discounting

• 成果类型: Article
• 署名作者: Jaskiewicz, Anna; Matkowski, J.; Nowak, A. S.
• 署名单位: Wroclaw University of Science & Technology; University of Zielona Gora
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X
• DOI: 10.1287/moor.1120.0561
• 发表日期: 2013
• 页码: 108-121
• 关键词: utility; uncertainty; mappings; GROWTH

摘要

In this paper we study a Markov decision process with a nonlinear discount function. First, we define a utility on the space of trajectories of the process in the finite and infinite time horizon and then take their expected values. It turns out that the associated optimization problem leads to a nonstationary dynamic programming and an infinite system of Bellman equations, which result in obtaining persistently optimal policies. Our theory is enriched by examples.