{"title":"有限状态部分可观察马尔可夫决策问题的动态风险度量","authors":"Jingnan Fan, A. Ruszczynski","doi":"10.1137/1.9781611974072.22","DOIUrl":null,"url":null,"abstract":"In this paper, we provide a theory of time-consistent dynamic risk measures for finite-state partially observable Markov decision problems. By employing our new concept of stochastic conditional time consistency, we show that such dynamic risk measures have a special structure, given by transition risk mappings as risk measures on the space of functionals on the observable state space only. Moreover, these mappings enjoy a strong monotonicity with respect to first order stochastic dominance.","PeriodicalId":193106,"journal":{"name":"SIAM Conf. on Control and its Applications","volume":"453 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Dynamic Risk Measures for Finite-State Partially Observable Markov Decision Problems\",\"authors\":\"Jingnan Fan, A. Ruszczynski\",\"doi\":\"10.1137/1.9781611974072.22\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we provide a theory of time-consistent dynamic risk measures for finite-state partially observable Markov decision problems. By employing our new concept of stochastic conditional time consistency, we show that such dynamic risk measures have a special structure, given by transition risk mappings as risk measures on the space of functionals on the observable state space only. Moreover, these mappings enjoy a strong monotonicity with respect to first order stochastic dominance.\",\"PeriodicalId\":193106,\"journal\":{\"name\":\"SIAM Conf. on Control and its Applications\",\"volume\":\"453 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SIAM Conf. on Control and its Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1137/1.9781611974072.22\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIAM Conf. on Control and its Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1137/1.9781611974072.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dynamic Risk Measures for Finite-State Partially Observable Markov Decision Problems
In this paper, we provide a theory of time-consistent dynamic risk measures for finite-state partially observable Markov decision problems. By employing our new concept of stochastic conditional time consistency, we show that such dynamic risk measures have a special structure, given by transition risk mappings as risk measures on the space of functionals on the observable state space only. Moreover, these mappings enjoy a strong monotonicity with respect to first order stochastic dominance.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
