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
Online Planning for Constrained POMDPs with Continuous Spaces through Dual Ascent
TL;DR: In this article , the authors propose algorithms for online constrained partially observable Markov decision process (CPOMDP) planning for continuous state, action, and observation spaces by combining dual ascent with progressive widening.
Dissertation
Autonomous Driving: A Multi-Objective Deep Reinforcement Learning Approach
TL;DR: It is shown that multi-objective reinforcement learning is effective at overcoming some of the difficulties faced by scalar-reward reinforcement learning, and a multi- objective DQN agent based on a variant of thresholded lexicographic Q-learning is successfully trained to drive on multi-lane roads and intersections, yielding and changing lanes according to traffic rules.
Proceedings Article
Pareto Local Search for MOMDP Planning.
TL;DR: Pareto local policy search (PLoPS) is proposed, a new planning method for MOMDPs based on Pareto Local Search (PLS), which produces a good set of policies by iteratively scanning the neighbourhood of locally non-dominated policies for improvements.
Proceedings ArticleDOI
Probabilistic Planning with Partially Ordered Preferences over Temporal Goals
TL;DR: It is proved that a weak- stochastic nondominated policy given the preference specification is Pareto-optimal in the constructed multi-objective MDP, and vice versa.
Journal ArticleDOI
Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models
TL;DR: A novel distributional multi-objective variable elimination (DMOVE) algorithm that is evaluated in realistic wind farm simulations and utilises a generative model known as real-NVP to learn the continuous return distributions to calculate the ESR set.
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