Journal ArticleDOI
Percentile Optimization for Markov Decision Processes with Parameter Uncertainty
Erick Delage,Shie Mannor +1 more
TLDR
A set of percentile criteria that are conceptually natural and representative of the trade-off between optimistic and pessimistic views of the question are presented and the use of these criteria under different forms of uncertainty for both the rewards and the transitions is studied.Abstract:
Markov decision processes are an effective tool in modeling decision making in uncertain dynamic environments. Because the parameters of these models typically are estimated from data or learned from experience, it is not surprising that the actual performance of a chosen strategy often differs significantly from the designer's initial expectations due to unavoidable modeling ambiguity. In this paper, we present a set of percentile criteria that are conceptually natural and representative of the trade-off between optimistic and pessimistic views of the question. We study the use of these criteria under different forms of uncertainty for both the rewards and the transitions. Some forms are shown to be efficiently solvable and others highly intractable. In each case, we outline solution concepts that take parametric uncertainty into account in the process of decision making.read more
Citations
More filters
Journal ArticleDOI
Theory and Applications of Robust Optimization
TL;DR: This paper surveys the primary research, both theoretical and applied, in the area of robust optimization (RO), focusing on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology.
Journal Article
Theory and Applications of Robust Optimization
TL;DR: In this article, the authors survey the primary research, both theoretical and applied, in the area of robust optimization and highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.
Journal Article
A comprehensive survey on safe reinforcement learning
Javier García,Fernando Fernández +1 more
TL;DR: This work categorize and analyze two approaches of Safe Reinforcement Learning, based on the modification of the optimality criterion, the classic discounted finite/infinite horizon, with a safety factor and the incorporation of external knowledge or the guidance of a risk metric.
Posted Content
Robust Adversarial Reinforcement Learning
TL;DR: RARL is proposed, where an agent is trained to operate in the presence of a destabilizing adversary that applies disturbance forces to the system and the jointly trained adversary is reinforced - that is, it learns an optimal destabilization policy.
Posted Content
Theory and Applications of Robust Optimization
TL;DR: In this paper, the authors survey the primary research, both theoretical and applied, in the area of robust optimization and highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.
References
More filters
Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
Journal ArticleDOI
Bayesian data analysis.
TL;DR: A fatal flaw of NHST is reviewed and some benefits of Bayesian data analysis are introduced and illustrative examples of multiple comparisons in Bayesian analysis of variance and Bayesian approaches to statistical power are presented.
MonographDOI
Markov Decision Processes
P. Whittle,M. L. Puterman +1 more
TL;DR: Markov Decision Processes covers recent research advances in such areas as countable state space models with average reward criterion, constrained models, and models with risk sensitive optimality criteria, and explores several topics that have received little or no attention in other books.
Neuro-Dynamic Programming.
TL;DR: In this article, the authors present the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.