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
Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSP
Ishaan Mehta,Sajad Saeedi +1 more
TL;DR: PA-Net is presented, a network that generates good approximations of the Pareto front for the bi-objective travelling salesperson problem (BTSP), and the application of PA-Net to find optimal visiting order in a robotic navigation task/coverage planning.
A Constrained Multi-Objective Reinforcement Learning Framework
Journal ArticleDOI
Neural scalarisation for multi-objective inverse reinforcement learning
Daiko Kishikawa,Sachiyo Arai +1 more
TL;DR: The authors proposed a multi-objective inverse reinforcement learning (MOIRL) method using neural scalarisation, which consists of weight mapping, reward, scalarization and weight back-translation.
Proceedings ArticleDOI
Reinforcement Learning Guided by Provable Normative Compliance
TL;DR: In this article , a normative supervisor is used to dynamically translate states and the applicable normative system into defeasible deontic logic theories, feed these theories to a theorem prover, and use the conclusions derived to decide whether or not to assign a punishment to the agent.
Proceedings ArticleDOI
Learning Synergies for Multi-Objective Optimization in Asymmetric Multiagent Systems
Gaurav Prasad Dixit,Kemal Tumer +1 more
TL;DR: The Multi-objective Asymmetric Island Model (MO-AIM) as mentioned in this paper is a multiobjective multiagent learning framework for the discovery of generalizable agent synergies and trade-offs that is based on adapting the population dynamics over a spectrum of tasks.
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