Distributionally robust optimization for sequential decision-making

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Distributionally Robust Optimization for Sequential Decision Making

The distributionally robust Markov Decision Process approach has been proposed in the literature, where the goal is to seek a distributionally robust policy that achieves the maximal expected total reward under the most adversarial joint distribution of uncertain parameters. In this paper, we study distributionally robust MDP where ambiguity sets for uncertain parameters are of a format that ca...

متن کامل

Distributionally Robust Convex Optimization

Distributionally robust optimization is a paradigm for decision-making under uncertaintywhere the uncertain problem data is governed by a probability distribution that is itself subjectto uncertainty. The distribution is then assumed to belong to an ambiguity set comprising alldistributions that are compatible with the decision maker’s prior information. In this paper,we propose...

متن کامل

Distributionally Robust Markov Decision Processes

We consider Markov decision processes where the values of the parameters are uncertain. This uncertainty is described by a sequence of nested sets (that is, each set contains the previous one), each of which corresponds to a probabilistic guarantee for a different confidence level so that a set of admissible probability distributions of the unknown parameters is specified. This formulation mode...

متن کامل

Calibration of Distributionally Robust Empirical Optimization Models

JUN-YA GOTOH, MICHAEL JONG KIM, AND ANDREW E.B. LIM Department of Industrial and Systems Engineering, Chuo University, Tokyo, Japan. Email: [email protected] Sauder School of Business, University of British Columbia, Vancouver, Canada. Email: [email protected] Departments of Decision Sciences and Finance, NUS Business School, National University of Singapore, Singapore. Email: andr...

متن کامل

Data-driven Distributionally Robust Polynomial Optimization

We consider robust optimization for polynomial optimization problems where the uncertainty set is a set of candidate probability density functions. This set is a ball around a density function estimated from data samples, i.e., it is data-driven and random. Polynomial optimization problems are inherently hard due to nonconvex objectives and constraints. However, we show that by employing polyno...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Optimization

سال: 2019

ISSN: 0233-1934,1029-4945

DOI: 10.1080/02331934.2019.1655738