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Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives.
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On the limitations of scalarisation for multi-objective reinforcement learning of Pareto fronts
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An empirical comparison of two common multiobjective reinforcement learning algorithms
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A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run
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Approximation of lorenz-optimal solutions in multiobjective markov decision processes
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Tree-based fitted q-iteration for multi-objective markov decision processes in water resource management
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A survey of multi-objective sequential decision-making
D. M. Roijers, P. Vamplew, S. Whiteson, and R. Dazeley · 2013
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Scalarized multi-objective reinforcement learning: Novel design techniques
K. Van Moffaert, M. M. Drugan, and A. Nowé · 2013
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Hypervolume indicator and dominance reward based multi-objective monte-carlo tree search
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Robust multiple objective game theory
H. Yu and H. Liu · 2013
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Multi-objective game theoretic scheduling of bag-of-tasks workflows on hybrid clouds
R. Duan, R. Prodan, and X. Li · 2014
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A dimensionality reduction approach for many-objective markov decision processes: Application to a water reservoir operation problem
Pedestrian simulation as multi-objective reinforcement learning
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Multi-objective reinforcement learning for the expected utility of the return
D. M. Roijers, D. Steckelmacher, and A. Nowé · 2018
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Pareto optimal solutions for network defense strategy selection simulator in multi-objective reinforcement learning
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Reinforcement learning: An introduction
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Modular multi-objective deep reinforcement learning with decision values
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M. Giuliani, S. Galelli, and R. Soncini-Sessa · 2014
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Queued pareto local search for multi-objective optimization
M. Inja, C. Kooijman, M. de Waard, D. M. Roijers, and S. Whiteson · 2014
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Set-valued dynamic treatment regimes for competing outcomes
E. B. Laber, D. J. Lizotte, and B. Ferguson · 2014
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Policy gradient approaches for multi-objective sequential decision making
S. Parisi, M. Pirotta, N. Smacchia, L. Bascetta, and M. Restelli · 2014
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Accelerating learning in multi-objective systems through transfer learning
A. Taylor, I. Dusparic, E. Galván-López, S. Clarke, and V. Cahill · 2014
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Multi-objective reinforcement learning using sets of pareto dominating policies
K. Van Moffaert and A. Nowé · 2014
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A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning
K. Van Moffaert, T. Brys, A. Chandra, L. Esterle, P. R. Lewis, and A. Nowé · 2014
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T. Tajmajer · 2018
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Multi-objective contextual bandit problem with similarity information
E. Turgay, D. Oner, and C. Tekin · 2018
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Human-aligned artificial intelligence is a multiobjective problem
P. Vamplew, R. Dazeley, C. Foale, S. Firmin, and J. Mummery · 2018
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Ordered preference elicitation strategies for supporting multi-objective decision making
L. M. Zintgraf, D. M. Roijers, S. Linders, C. M. Jonker, and A. Nowé · 2018
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Intrinsically motivated hierarchical policy learning in multi-objective markov decision processes
S. Abdelfattah, K. Merrick, and J. Hu · 2019
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Dynamic weights in multi-objective deep reinforcement learning
A. Abels, D. Roijers, T. Lenaerts, A. Nowé, and D. Steckelmacher · 2019
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Aggregation of dependent criteria in multicriteria decision making problems by means of capacities
G. Beliakov, S. Bowsell, T. Cao, R. Dazeley, V. Mak-Hau, M.-T. Nguyen, T. Wilkin, and J. Yearwood · 2019
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Universal successor features approximators
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Pareto monte carlo tree search for multi-objective informative planning
W. Chen and L. Liu · 2019
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Meta-learning for multi-objective reinforcement learning
X. Chen, A. Ghadirzadeh, M. Björkman, and P. Jensfelt · 2019
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Memory-based explainable reinforcement learning
F. Cruz, R. Dazeley, and P. Vamplew · 2019
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Multi-objective reinforcement learning for reconfiguring data stream analytics on edge computing
A. da Silva Veith, F. R. de Souza, M. D. de Assunção, L. Lefèvre, and J. C. S. dos Anjos · 2019
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Reinforcement learning for satellite communications: from leo to deep space operations
P. V. R. Ferreira, R. Paffenroth, A. M. Wyglinski, T. M. Hackett, S. G. Bilen, R. C. Reinhart, and D. J. Mortensen · 2019
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Applying reinforcement learning to plan manufacturing material handling part 1: Background and formal problem specification
S. Govindaiah and M. D. Petty · 2019
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Dynamic multi-objective optimisation using deep reinforcement learning: benchmark, algorithm and an application to identify vulnerable zones based on water quality
M. M. Hasan, K. Lwin, M. Imani, A. Shabut, L. F. Bittencourt, and M. A. Hossain · 2019
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S. H. Huang, M. Zambelli, J. Kay, M. F. Martins, Y. Tassa, P. M. Pilarski, and R. Hadsell · 2019
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