On the Asymptotic Optimality of Finite Approximations to Markov Decision Processes with Borel Spaces

• 成果类型: Article
• 署名作者: Saldi, Naci; Yuksel, Serdar; Linder, Tamas
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; Queens University - Canada
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X
• DOI: 10.1287/moor.2016.0832
• 发表日期: 2017
• 页码: 945-978
• 关键词: state approximations; CONVERGENCE; performance; continuity; equation; policies

摘要

Calculating optimal policies is known to be computationally difficult for Markov decision processes (MDPs) with Borel state and action spaces. This paper studies finite-state approximations of discrete time Markov decision processes with Borel state and action spaces, for both discounted and average costs criteria. The stationary policies thus obtained are shown to approximate the optimal stationary policy with arbitrary precision under quite general conditions for discounted cost and more restrictive conditions for average cost. For compact-state MDPs, we obtain explicit rate of convergence bounds quantifying how the approximation improves as the size of the approximating finite state space increases. Using information theoretic arguments, the order optimality of the obtained convergence rates is established for a large class of problems. We also show that as a pre-processing step, the action space can also be finitely approximated with sufficiently large number points; thereby, well known algorithms, such as value or policy iteration, Q-learning, etc., can be used to calculate near optimal policies.