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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On the Practical Art of State Definitions for Markov Decision Process Construction
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A Reinforcement Learning based evolutionary multi-objective optimization algorithm for spectrum allocation in Cognitive Radio networks
Amandeep Kaur,Krishan Kumar +1 more
TL;DR: This paper addresses the spectrum allocation problem concerning network capacity and spectrum efficiency as conflicting objectives and model the scenario as a multi-objective optimization problem in CR networks and proposes an improved version of the Non-dominated Sorting Genetic Algorithm which incorporates a self-tuning parameter approach to handle multiple conflicting objectives.
Journal ArticleDOI
Learning adversarial attack policies through multi-objective reinforcement learning
TL;DR: A novel modelization of the process of learning an attack policy as a Multi-objective Markov Decision Process with two objectives: maximizing the performance loss of the attacked policy and minimizing the cost of the attacks.
Journal ArticleDOI
Opponent learning awareness and modelling in multi-objective normal form games
Roxana Radulescu,Timothy Verstraeten,Yijie Zhang,Patrick Mannion,Diederik M. Roijers,Ann Nowé +5 more
TL;DR: In this paper, the effects of opponent learning awareness on multi-objective multi-agent interactions with nonlinear utilities were studied and the actor-critic and policy gradient formulations were extended to allow reinforcement learning of mixed strategies in this setting.
Proceedings ArticleDOI
Agent Coordination in Air Combat Simulation using Multi-Agent Deep Reinforcement Learning
Johan Källström,Fredrik Heintz +1 more
TL;DR: This work empirically evaluate a number of approaches in two air combat scenarios, and demonstrates that curriculum learning is a promising approach for handling the high-dimensional state space of the air combat domain, and that multi-objective learning can produce synthetic agents with diverse characteristics, which can stimulate human pilots in training.
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