A survey of multi-objective sequential decision-making
TLDR
This article surveys algorithms designed for sequential decision-making problems with multiple objectives and proposes a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function, and the type of policies considered.Abstract:
Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-objective problems. Therefore, we identify three distinct scenarios in which converting such a problem to a single-objective one is impossible, infeasible, or undesirable. Furthermore, we propose a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function (which projects multi-objective values to scalar ones), and the type of policies considered. We show how these factors determine the nature of an optimal solution, which can be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we survey the literature on multi-objective methods for planning and learning. Finally, we discuss key applications of such methods and outline opportunities for future work.read more
Citations
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DissertationDOI
Data-driven decision-making and its application to the corporate cash management problem
TL;DR: In this article, a multi-objective approach is proposed to solve the multiple bank accounts cash management problem. But the authors focus on both the cost and risk of the decision-making process.
Posted Content
Convex Hull Monte-Carlo Tree Search
TL;DR: This work considers how to pose the problem of approximating multiobjective planning solutions as a contextual multi-armed bandits problem, giving a principled motivation for how to select actions from the view of contextual regret, leading to the use of Contextual Zooming for action selection, yielding Zooming CHMCTS.
Book ChapterDOI
A Multi-objective Reinforcement Learning Algorithm for JSSP
Beatriz M. Méndez-Hernández,Erick D. Rodríguez-Bazan,Yailen Martínez-Jiménez,Pieter Libin,Ann Nowé +4 more
TL;DR: A Multi-Objective Multi-Agent Reinforcement Learning Algorithm that aims to obtain the non-dominated solutions set for Job Shop scheduling problems and is evaluated and compared to other algorithms from the literature using two measures for evaluating the Pareto front.
Journal ArticleDOI
Learning and decision-making in artificial animals
TL;DR: The animat framework enables a uniform and gradual approach to AGI, by successively taking on more challenging problems in the form of broader and more complex classes of environments.
Posted Content
No-regret Algorithms for Multi-task Bayesian Optimization
TL;DR: This work addresses the problem of inter-task dependencies using a multi-task kernel and develops two novel BO algorithms based on random scalarizations of the objectives that belong to the upper confidence bound class of algorithms.
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