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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Journal ArticleDOI
A practical guide to multi-objective reinforcement learning and planning
Conor Hayes,Roxana Radulescu,Eugenio Bargiacchi,John Källström,Matthew Macfarlane,Mathieu Reymond,Timothy Verstraeten,Luisa M. Zintgraf,Richard Dazeley,Fredrik Heintz,Enda Howley,Athirai A. Irissappane,Patrick Mannion,Ann Nowé,Gabriel Ramos,Marcello Restelli,Peter Vamplew,Diederik M. Roijers +17 more
TL;DR: In this article , a guide to the application of multi-objective decision-making methods to difficult problems is presented, aimed at researchers who are already familiar with singleobjective reinforcement learning and planning methods and who wish to adopt a multiobjective perspective on their research.
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Offline Contextual Bandits with High Probability Fairness Guarantees
Blossom Metevier,Stephen Giguere,Sarah Brockman,Ari Kobren,Yuriy Brun,Emma Brunskill,Philip S. Thomas +6 more
TL;DR: This work provides a theoretical analysis of RobinHood, an offline contextual bandit algorithm designed to satisfy a broad family of fairness constraints, and provides a proof that it will not return an unfair solution with probability greater than a user-specified threshold.
Journal ArticleDOI
Manifold-based multi-objective policy search with sample reuse
TL;DR: Two novel manifold-based algorithms to solve multi-objective Markov decision processes that combine episodic exploration strategies and importance sampling to efficiently learn a manifold in the policy parameter space such that its image in the objective space accurately approximates the Pareto frontier are presented.
Proceedings Article
Pareto Monte Carlo Tree Search for Multi-Objective Informative Planning
Weizhe Chen,Lantao Liu +1 more
TL;DR: An anytime multi-objective informative planning method called Pareto Monte Carlo tree search which allows the robot to handle potentially competing objectives such as exploration versus exploitation and produces optimized decision solutions for the robot based on its knowledge of the environment state.
Journal ArticleDOI
Multi-objectivization and ensembles of shapings in reinforcement learning
TL;DR: The combination of multi-objectivization and ensemble techniques are argued for as a powerful tool to boost solving performance in reinforcement learning, injecting various pieces of heuristic information through reward shaping, creating several distinct enriched reward signals.
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