Article

Multi-criteria Reinforcement Learning

Authors:

Zoltán Gábor,

Zsolt Kalmár,

Csaba SzepesváriAuthors Info & Claims

ICML '98: Proceedings of the Fifteenth International Conference on Machine Learning

Pages 197 - 205

Published: 24 July 1998 Publication History

Abstract

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Li CCzarnecki KElkind EVeloso MAgmon NTaylor M(2019)Urban Driving with Multi-Objective Deep Reinforcement LearningProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331714(359-367)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3331714
Chow YNachum ODuenez-Guzman EGhavamzadeh M(2018)A lyapunov-based approach to safe reinforcement learningProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327757.3327904(8103-8112)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3327757.3327904
Desai NCritch ARussell S(2018)Negotiable reinforcement learning for pareto optimal sequential decision-makingProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327345.3327381(4717-4725)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3327345.3327381
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Index Terms

Multi-criteria Reinforcement Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning settings

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Published In

ICML '98: Proceedings of the Fifteenth International Conference on Machine Learning

July 1998

573 pages

ISBN:1558605568

Editor:
Jude W. Shavlik

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

Publication History

Published: 24 July 1998

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Li CCzarnecki KElkind EVeloso MAgmon NTaylor M(2019)Urban Driving with Multi-Objective Deep Reinforcement LearningProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331714(359-367)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3331714
Chow YNachum ODuenez-Guzman EGhavamzadeh M(2018)A lyapunov-based approach to safe reinforcement learningProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327757.3327904(8103-8112)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3327757.3327904
Desai NCritch ARussell S(2018)Negotiable reinforcement learning for pareto optimal sequential decision-makingProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327345.3327381(4717-4725)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3327345.3327381
Busa-Fekete RSzörényi BWeng PMannor S(2017)Multi-objective banditsProceedings of the 34th International Conference on Machine Learning - Volume 7010.5555/3305381.3305446(625-634)Online publication date: 6-Aug-2017
https://dl.acm.org/doi/10.5555/3305381.3305446
García JIglesias RRodríguez MRegueiro C(2017)Incremental reinforcement learning for multi-objective robotic tasksKnowledge and Information Systems10.1007/s10115-016-0992-251:3(911-940)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10115-016-0992-2
Wray KZilberstein S(2015)Multi-objective POMDPs with lexicographic reward preferencesProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832415.2832488(1719-1725)Online publication date: 25-Jul-2015
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Drugan MEsparcia-Alcázar A(2015)Synergies between Evolutionary Algorithms and Reinforcement LearningProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2756582(723-740)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739482.2756582
Mouaddib A(2012)Vector-Value Markov Decision Process for multi-objective stochastic path planningInternational Journal of Hybrid Intelligent Systems10.3233/HIS-2012-01469:1(45-60)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.3233/HIS-2012-0146
Issabekov RVamplew P(2012)An empirical comparison of two common multiobjective reinforcement learning algorithmsProceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence10.1007/978-3-642-35101-3_53(626-636)Online publication date: 4-Dec-2012
https://dl.acm.org/doi/10.1007/978-3-642-35101-3_53
Gosavi APurohit M(2011)Stochastic policy search for variance-penalized semi-Markov controlProceedings of the Winter Simulation Conference10.5555/2431518.2431858(2865-2876)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2431858
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Recommendations

Multi-objective safe reinforcement learning: the relationship between multi-objective reinforcement learning and safe reinforcement learning

Reward Shaping in Episodic Reinforcement Learning

Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python

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