期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 59, 期 9, 页码 2574-2579出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2014.2309262
关键词
Constrained Markov decision processes; risk measures; stochastic approximations
资金
- NSF [IIS-0917410]
- NSF CAREER Award [CNS-0954116]
- ONR Young Investigator Award [N000141210766]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [0954116] Funding Source: National Science Foundation
We propose a new constrained Markov decision process framework with risk-type constraints. The risk metric we use is Conditional Value-at-Risk (CVaR), which is gaining popularity in finance. It is a conditional expectation but the conditioning is defined in terms of the level of the tail probability. We propose an iterative offline algorithm to find the risk-contrained optimal control policy. A two time-scale stochastic approximation-inspired 'learning' variant is also sketched, and its convergence proved to the optimal risk-constrained policy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据