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Multi-objective reinforcement learning (MORL) is an extension of ordinary, single-objective reinforcement learning (RL) that is applicable to many real-world tasks where multiple objectives exist without known relative costs.
Multi-objective infinite-horizon discounted Markov decision processes
D. J. White · 1982
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Multi-criteria reinforcement learning
Zoltán Gábor, Zsolt Kalmár, and Csaba Szepesvári · 1998
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The measure of Pareto optima: applications to multi-objective metaheuristics
M. Fleischer · 2003
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A geometric approach to multi-criterion reinforcement learning
Shie Mannor and Nahum Shimkin · 2004
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Dynamic preferences in multi-criteria reinforcement learning
Sriraam Natarajan and Prasad Tadepalli · 2005
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Managing power consumption and performance of computing systems using reinforcement learning
Gerald Tesauro, Rajarshi Das, Hoi Chan, Jeffrey O. Kephart, Charles Lefurgy, David W. Levine, and Freeman Rawson · 2007
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Learning all optimal policies with multiple criteria
Leon Barrett and Srini Narayanan · 2008
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Multi-objective fitted Q {Q} -iteration: Pareto frontier approximation in one single run
Andrea Castelletti, Francesca Pianosi, and Marcello Restelli · 2011
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Empirical evaluation methods for multiobjective reinforcement learning algorithms
Peter Vamplew, Richard Dazeley, Adam Berry, Rustam Issabekov, and Evan Dekker · 2011
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Tree-based fitted Q-iteration for multi-objective Markov decision problems
Andrea Castelletti, Francesca Pianosi, and Marcello Restelli · 2012
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Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model
Mohammad Gheshlaghi Azar, Rémi Munos, and Hilbert J Kappen · 2013
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A survey of multi-objective sequential decision-making
Diederik M Roijers, Peter Vamplew, Shimon Whiteson, and Richard Dazeley · 2013
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Multiobjective reinforcement learning: A comprehensive overview
Chunming Liu, Xin Xu, and Dewen Hu · 2014
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie amd Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Fair lending needs explainable models for responsible recommendation
Jiahao Chen · 2018
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Regret minimization for reinforcement learning with vectorial feedback and complex objectives
Wang Chi Cheung · 2019
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Challenges of real-world reinforcement learning
Gabriel Dulac-Arnold, Daniel Mankowitz, and Todd Hester · 2019
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Kristof Van Moffaert, Madalina M Drugan, and Ann Nowé · 2013
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Hypervolume indicator and dominance reward based multi-objective Monte-Carlo tree search
Weijia Wang and Michèle Sebag · 2013
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Runzhe Yang, Xingyuan Sun, and Karthik Narasimhan · 2019
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Fair lending needs explainable models for responsible recommendation
Eren Kurshan, Hongda Shen, and Jiahao Chen · 2020
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