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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Book ChapterDOI
Multiple criteria decision making
János Fodor,Marc Roubens +1 more
TL;DR: In this Chapter, a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view is imagined.
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Multi-Task Learning as Multi-Objective Optimization
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References
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Proceedings ArticleDOI
Distributed W-Learning: Multi-Policy Optimization in Self-Organizing Systems
Ivana Dusparic,Vinny Cahill +1 more
TL;DR: Distributed W-Learning is a reinforcement learning (RL)-based algorithm for collaborative agent-based self-optimization towards multiple policies, which relies only on local interactions and learning and can improve the performance of multiple policies deployed simultaneously, even over corresponding single-policy deployments.
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Reinforcement Learning for Call Admission Control and Routing under Quality of Service Constraints in Multimedia Networks
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Proceedings Article
The Steering Approach for Multi-Criteria Reinforcement Learning
Shie Mannor,Nahum Shimkin +1 more
TL;DR: An algorithm for achieving this task, which is based on the theory of approachability for stochastic games, is devised, in an appropriate way, a finite set of standard, scalar-reward learning algorithms.
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Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes
TL;DR: It is proved that the decision problems for both expectation and satisfaction objectives can be solved in polynomial time and the trade-off curve (Pareto curve) can be epsilon-approximated in timePolynomial in the size of the MDP and 1/epsilon, and exponential in the number of reward functions, for all epsilus>0.
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