Deep reinforcement learning for multi-objective game strategy selection

R Jiang, Y Deng, Y Chen, H Luo, B An - Computers & Operations Research, 2024 - Elsevier
Multi-objective game (MOG) is a fundamental model for the decision-making problems in
which each player must consider multi-dimensional payoffs that reflect different objectives …

Multi-objective evolutionary algorithms: past, present, and future

CAC Coello, SG Brambila, JF Gamboa… - Black Box Optimization …, 2021 - Springer
Evolutionary algorithms have become a popular choice for solving highly complex multi-
objective optimization problems in recent years. Multi-objective evolutionary algorithms were …

Rationalizable strategies for the navigator–target–missile game

M Harel, A Moshaiov, D Alkaher - Journal of Guidance, Control, and …, 2020 - arc.aiaa.org
This paper concerns an aerial three-body multiobjective game (MOG) and deals with finding
and sorting the control strategies by a novel solution concept. The MOG is a biobjective …

Solving zero‐sum multi‐objective games with a‐priori secondary criteria

M Harel, E Eisenstadt‐Matalon… - Journal of Multi‐Criteria …, 2023 - Wiley Online Library
Solving non‐cooperative zero‐sum multi‐objective Games (zsMOGs), under undecided
objective preferences results, for each of the players, in a Set of Rationalizable Strategies …

Decision‐making in non‐cooperative games with conflicting self‐objectives

E Eisenstadt, A Moshaiov - Journal of Multi‐Criteria Decision …, 2018 - Wiley Online Library
This paper concerns multicriteria decision making by players in a conflict situation. The
considered situation is modelled as a non‐cooperative game. Moreover, the player has a …

Decision analysis of rationalizable strategies in non-zero-sum multi-payoff games

E Eisenstadt-Matalon… - Intelligent Decision …, 2022 - content.iospress.com
This paper concerns multi-criteria decision-making in a non-cooperative situation between
two Decision Makers (DMs), where each of the DMs (players) has self-conflicting objectives …