APRIL: Active Preference Learning-Based Reinforcement Learning
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Akrour et al. consider continuous domains with four degrees of freedom and small discrete domains, where they can assume that the reward is linear in the expectations of hand-coded features.
“They consider continuous domains with four degrees of freedom and small discrete domains, where they can assume that the reward is linear in the expectations of hand-coded features.”
From Deep reinforcement learning from human preferences (2017) · cites this paper as Akrour et al. (2012)
Section 4.2 (pp. 11–13) studies a continuous-action cancer-treatment problem and a discrete-action mountain-car problem, but reports policy search spaces of 397 and 37 dimensions, not continuous domains with four degrees of freedom. It also says neither benchmark has a reward function. Section 3.1 (p. 7) says the behavioral features are derived from clustered sensor and actuator data without domain knowledge, contrary to the claim that the method assumes a reward linear in hand-coded features. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredOur algorithm follows the same basic approach as Akrour et al. (2012) and Akrour et al. (2014).
“Our algorithm follows the same basic approach as Akrour et al. (2012) and Akrour et al. (2014).”
From Deep reinforcement learning from human preferences (2017) · cites this paper as Akrour et al. (2012)
Akrour et al. elicit preferences over whole trajectories rather than short clips.
“One notable difference is that Akrour et al. (2012) and Akrour et al. (2014) elicit preferences over whole trajectories rather than short clips.”
From Deep reinforcement learning from human preferences (2017) · cites this paper as Akrour et al. (2012)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
Pattern classification and scene analysis
Duda, R. O., & Hart, P. E. (1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895 · see citing works
Efficient Global Optimization of Expensive Black-Box Functions
Jones, D. R., Schonlau, M., & Welch, W. J. (1998) · Journal of Global Optimization · doi:10.1023/a:1008306431147 · Cited by 8,070 · see citing works
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855 · see citing works
Completely Derandomized Self-Adaptation in Evolution Strategies
Hansen, N., & Ostermeier, A. (2001) · Evolutionary Computation · doi:10.1162/106365601750190398 · Cited by 4,397 · see citing works
Neural Information Processing Systems
(2018) · The MIT Press eBooks · doi:10.7551/mitpress/11474.003.0014 · Cited by 3,775 · see citing works
Pattern classification and scene analysis
Chien, Y. P. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100577 · Cited by 3,413 · see citing works
A sensorimotor account of vision and visual consciousness
O'Regan, J. K., & Noë, A. (2001) · Behavioral and Brain Sciences · doi:10.1017/s0140525x01000115 · Cited by 3,126 · see citing works
Apprenticeship learning via inverse reinforcement learning
Abbeel, P., & Ng, A. Y. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015430 · Cited by 2,855 · see citing works
Training linear SVMs in linear time
Joachims, T. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150429 · Cited by 1,932 · see citing works
Large Margin Methods for Structured and Interdependent Output Variables
Tsochantaridis, I., Joachims, T., Hofmann, T., & Altün, Y. (2005) · MPG.PuRe (Max Planck Society) · Cited by 1,879 · see citing works
(2000) · doi:10.2460/ajvr.67.2.323 · Cited by 1,621 · see citing works
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
Calinon, S., Guenter, F., & Billard, A. (2007) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2006.886952 · Cited by 1,090 · see citing works
Reinforcement learning of motor skills with policy gradients
Peters, J., & Schaal, S. (2008) · Neural Networks · doi:10.1016/j.neunet.2008.02.003 · Cited by 831 · see citing works
A support vector method for multivariate performance measures
Joachims, T. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102399 · Cited by 795 · see citing works
Predictive Representations of State
Littman, M. L., & Sutton, R. S. (2001) · Cited by 424 · see citing works
Algorithms for reinforcement learning
Szepesvári, C. (2010) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01551-9 · Cited by 308 · see citing works
Robot learning from demonstration by constructing skill trees
Konidaris, G., Kuindersma, S., Grupen, R. A., & Barto, A. G. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364911428653 · Cited by 287 · see citing works
Reinforcement learning design for cancer clinical trials
Zhao, Y., Kosorok, M. R., & Zeng, D. (2009) · Statistics in Medicine · doi:10.1002/sim.3720 · Cited by 273 · see citing works
Coarse sample complexity bounds for active learning
Dasgupta, S. (2005) · Cited by 240 · see citing works
Active Preference Learning with Discrete Choice Data
Brochu, E., de Freitas, N., & Ghosh, A. (2007) · Cited by 125 · see citing works
Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion
Kolter, J. Z., Abbeel, P., & Ng, A. Y. (2007) · Cited by 122 · see citing works
Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search
Heidrich-Meisner, V., & Igel, C. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553426 · Cited by 120 · see citing works
Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets
Viappiani, P., & Boutilier, C. E. (2010) · neural information processing systems · Cited by 83 · see citing works
Preference-Based Policy Learning
Akrour, R., Schoenauer, M., & Sébag, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23780-5_11 · Cited by 82 · see citing works
Self-citationConstructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories
Konidaris, G., Kuindersma, S., Grupen, R. A., & Barreto, A. S. (2010) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 81 · see citing works
Bergeron, C., Zaretzki, J. M., Breneman, C. M., & Bennett, K. P. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390163 · Cited by 44 · see citing works
Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning
Cheng, W., Fürnkranz, J., Hüllermeier, E., & Park, S.-H. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23780-5_30 · Cited by 39 · see citing works
Whiteson, S., Taylor, M. E., & Stone, P. (2009) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-009-9100-2 · Cited by 30 · see citing works
Hachiya, H., & Sugiyama, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15880-3_36 · Cited by 24 · see citing works
Locomotion control of quadruped robots based on CPG-inspired workspace trajectory generation
Liu, C., Chen, Q., & Wang, D. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5979576 · Cited by 8 · see citing works
Monte Carlo Methods for Preference Learning
Viappiani, P. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34413-8_52 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [10]10. Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.): Proc. European Conf. on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, Part I. LNCS 6911, Springer Verlag (2011)
- [14]14. Herbrich, R., Graepel, T., Campbell, C.: Bayes point machines. Journal of Machine Learning Research 1, 245-279 (2001)
- [20]20. Lagoudakis, M., Parr, R.: Least-squares policy iteration. Journal of Machine Learning Research (JMLR) 4, 1107-1149 (2003)
Cited by
Sorted by citations
Deep reinforcement learning from human preferences
Christiano, P., Leike, J., Brown, T. B., Martic, M., Legg, S., & Amodei, D. (2017) · arXiv · doi:10.48550/arxiv.1706.03741 · Cited by 1,058 · see citing works
Reinforcement Learning in Healthcare: A Survey
Yu, C., Liu, J.-M., Nemati, S., & Yin, G. (2019) · ACM Computing Surveys · doi:10.1145/3477600 · Cited by 851 · see citing works
Active Preference-Based Learning of Reward Functions
Sadigh, D., Dragan, A. D., Sastry, S., & Seshia, S. A. (2017) · Robotics: Science and Systems · doi:10.15607/rss.2017.xiii.053 · Cited by 260 · see citing works
Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
Icarte, R. T., Klassen, T. Q., Valenzano, R., & McIlraith, S. A. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12440 · Cited by 190 · see citing works
Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications
Dias, P. G. F., Silva, M. C., Filho, G. P. R., Vargas, P. A., Cota, L. P., & Pessin, G. (2021) · Sensors · doi:10.3390/s21062062 · Cited by 125 · see citing works
Learning Reward Functions by Integrating Human Demonstrations and Preferences
Palan, M., Shevchuk, G., Landolfi, N. C., & Sadigh, D. (2019) · Robotics: Science and Systems · doi:10.15607/rss.2019.xv.023 · Cited by 106 · see citing works
Bıyık, E., Losey, D. P., Palan, M., Landolfi, N. C., Shevchuk, G., & Sadigh, D. (2021) · The International Journal of Robotics Research · doi:10.1177/02783649211041652 · Cited by 95 · see citing works
Asking Easy Questions: A User-Friendly Approach to Active Reward LearningPreprint
Biyik, E., Palan, M., Landolfi, N. C., Losey, D. P., & Sadigh, D. (2019) · Conference on Robot Learning · doi:10.48550/arxiv.1910.04365 · Cited by 151 · see citing works
Lee, K., Smith, L., & Abbeel, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05091 · Cited by 86 · see citing works
Model-Free Preference-Based Reinforcement Learning
Wirth, C., Fürnkranz, J., & Neumann, G. M. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10269 · Cited by 64 · see citing works
Active Preference-Based Gaussian Process Regression for Reward Learning
Bıyık, E., Huynh, N., Kochenderfer, M. J., & Sadigh, D. (2020) · Robotics: Science and Systems · doi:10.15607/rss.2020.xvi.041 · Cited by 51 · see citing works
Global optimization based on active preference learning with radial basis functions
Bemporad, A., & Piga, D. (2020) · Machine Learning · doi:10.1007/s10994-020-05935-y · Cited by 52 · see citing works
Teacher-Student Framework: a Reinforcement Learning Approach
Zimmer, M., Viappiani, P., & Weng, P. (2014) · HAL · Cited by 50 · see citing works
Guiding Policies with Language via Meta-LearningPreprint
Co-Reyes, J. D., Gupta, A., Sanjeev, S., Altieri, N., Andreas, J., DeNero, J., Abbeel, P., & Levine, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.07882 · Cited by 41 · see citing works
Multi-objective Reinforcement Learning for the Expected Utility of the Return
Roijers, D. M., Steckelmacher, D., & Nowé, A. (2018) · VU Research Portal · Cited by 32 · see citing works
Active preference-based Gaussian process regression for reward learning and optimization
Biyik, E., Huynh, N., Kochenderfer, M. J., & Sadigh, D. (2020) · Robotics: Science and Systems Conference · doi:10.1177/02783649231208729 · Cited by 154 · see citing works
Toward Grounded Commonsense Reasoning
Kwon, M., Hu, H., Myers, V., Karamcheti, S., Dragan, A. D., & Sadigh, D. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611218 · Cited by 32 · see citing works
Adaptive artificial companions learning from users’ feedback
Karami, A., Sehaba, K., & Encelle, B. (2016) · Adaptive Behavior · doi:10.1177/1059712316634062 · Cited by 29 · see citing works
Wilde, N., Blidaru, A., Smith, S. L., & Kulić, D. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920910802 · Cited by 29 · see citing works
Zhan, H., Tao, F., & Cao, Y. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3063927 · Cited by 29 · see citing works
Busa‐Fekete, R., Szörényi, B., Weng, P., Cheng, W., & Hüllermeier, E. (2014) · Machine Learning · doi:10.1007/s10994-014-5458-8 · Cited by 29 · see citing works
Active Learning of Reward Dynamics from Hierarchical Queries
Basu, C., Bıyık, E., He, Z., Singhal, M., & Sadigh, D. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968522 · Cited by 28 · see citing works
Karami, A., Fleury, A., Boonært, J., & Lecœuche, S. (2016) · Information · doi:10.3390/info7020035 · Cited by 28 · see citing works
The Green Choice: Learning and Influencing Human Decisions on Shared Roads
Bıyık, E., Lazar, D. A., Sadigh, D., & Pedarsani, R. (2019) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc40024.2019.9030169 · Cited by 26 · see citing works
Learning from Richer Human Guidance
Basu, C., Singhal, M., & Dragan, A. D. (2018) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3171221.3171284 · Cited by 24 · see citing works
Jiang, D., Huang, J., Fang, Z., Cheng, C., Sha, Q., He, B., & Li, G. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.111971 · Cited by 22 · see citing works
Schoenauer, M., Akrour, R., Sébag, M., & Souplet, J.-C. (2014) · International Conference on Machine Learning · Cited by 22 · see citing works
Self-citationZaniolo, M., Giuliani, M., & Castelletti, A. F. (2021) · Water Resources Research · doi:10.1029/2020wr029329 · Cited by 21 · see citing works
Nahian, M. S. A., Frazier, S., Riedl, M. O., & Harrison, B. E. (2020) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3375627.3375825 · Cited by 26 · see citing works
The value of adaptive menu sizes in peer-to-peer platforms
Karabulut, E., Gholizadeh, F., & Akhavan‐Tabatabaei, R. (2022) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2022.103948 · Cited by 16 · see citing works
Machine learning for interactive systems and robots
Cuayáhuitl, H., van Otterlo, M., Dethlefs, N., & Frommberger, L. (2013) · doi:10.1145/2493525.2493530 · Cited by 16 · see citing works
On the equivalence of optimal recommendation sets and myopically optimal query sets
Viappiani, P., & Boutilier, C. E. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103328 · Cited by 16 · see citing works
Reinforcement Learning with Human Feedback for Realistic Traffic Simulation
Cao, Y., Ivanovic, B., Xiao, C., & Pavone, M. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610878 · Cited by 17 · see citing works
Hüllermeier, E., & Słowiński, R. (2024) · 4OR · doi:10.1007/s10288-023-00561-5 · Cited by 14 · see citing works
Flocking-Based Self-Organized Aggregation Behavior Method for Swarm Robotics
Mısır, O., & Gökrem, L. (2021) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-021-00442-9 · Cited by 14 · see citing works
Marta, D., Holk, S., Pek, C., Tůmová, J., & Leite, I. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341795 · Cited by 13 · see citing works
Interactive Policy Shaping for Human-Robot Collaboration with Transparent Matrix Overlays
Brawer, J., Ghose, D., Candon, K., Qin, M., Roncone, A., Vázquez, M., & Scassellati, B. M. (2023) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3568162.3576983 · Cited by 13 · see citing works
Learning from Richer Human Guidance: Augmenting Comparison-Based Learning with Feature QueriesPreprint
Basu, C., Singhal, M., & Dragan, A. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.01604 · Cited by 13 · see citing works
Multi-Device Nutrition Control
Cunha, C. A. D. S., & Duarte, R. P. (2022) · Sensors · doi:10.3390/s22072617 · Cited by 12 · see citing works
Preference-Based Reinforcement Learning: A Preliminary Survey
Wirth, C., & Fürnkranz, J. (2013) · Cited by 12 · see citing works
Task Decoupling in Preference-based Reinforcement Learning for Personalized Human-Robot Interaction
Liu, M., & Chen, C. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981076 · Cited by 10 · see citing works
Batch Active Learning of Reward Functions from Human Preferences
Bıyık, E., Anari, N., & Sadigh, D. (2024) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3649885 · Cited by 9 · see citing works
Interactive Reinforcement Learning from Imperfect Teachers
Faulkner, T. K., & Thomaz, A. L. (2021) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3434074.3446361 · Cited by 9 · see citing works
Dual-View Preference Learning for Adaptive Recommendation
Liu, Z., Fang, Y., & Wu, M. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3236370 · Cited by 6 · see citing works
Preference-based Evolutionary Direct Policy Search
Busa‐Fekete, R., Szörényi, B., Weng, P., Cheng, W., & Hüllermeier, E. (2013) · Cited by 9 · see citing works
Active Inverse Reward DesignPreprint
Mindermann, S., Shah, R., Gleave, A., & Hadfield-Menell, D. (2018) · arXiv.org · doi:10.48550/arxiv.1809.03060 · Cited by 23 · see citing works
Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences
Hwang, M., Weihs, L., Park, C., Lee, K., Kembhavi, A., & Ehsani, K. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01535 · Cited by 9 · see citing works
Active Attention-Modified Policy Shaping: Socially Interactive Agents Track
Faulkner, T. K., Gutierrez, R. A., Short, E. S., Hoffman, G., & Thomaz, A. L. (2019) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/qzlb8105 · Cited by 8 · see citing works
Pitfalls of Learning a Reward Function Online
Armstrong, S., Leike, J., Orseau, L., & Legg, S. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/221 · Cited by 8 · see citing works
Fine-Tuning a Transformer-Based Language Model to Avoid Generating Non-Normative Text.Preprint
Peng, X., Li, S., Frazier, S., & Riedl, M. O. (2020) · arXiv (Cornell University) · Cited by 8 · see citing works
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior
Nahian, M. S. A., Frazier, S., Riedl, M. O., & Harrison, B. E. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3363122 · Cited by 13 · see citing works
Querying to Find a Safe Policy under Uncertain Safety Constraints in Markov Decision Processes
Zhang, S., Durfee, E. H., & Singh, S. P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5638 · Cited by 7 · see citing works
Active Measure Reinforcement Learning for Observation Cost Minimization
Bellinger, C., Coles, R., Crowley, M., & Tamblyn, I. (2021) · doi:10.21428/594757db.72846d04 · Cited by 7 · see citing works
Interactive Imitation Learning in Robotics: A Survey
Celemin, C. E., Pérez‐Dattari, R., Chisari, E., Franzese, G., Rosa, L. D. S., Prakash, R., Ajanović, Z., Ferraz, M., Valada, A., & Kober, J. (2022) · doi:10.1561/9781638281276 · Cited by 7 · see citing works
Ran, W., Chen, H., Xia, T., Nishimura, Y., Guo, C., & Yin, Y. (2023) · Sensors · doi:10.3390/s23115246 · Cited by 7 · see citing works
EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning
Wirth, C., & Fürnkranz, J. (2013) · Cited by 7 · see citing works
Search-Based Software Engineering
Goues, C. L., & Yoo, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09940-8 · Cited by 7 · see citing works
Active Preference-Based Gaussian Process Regression for Reward LearningPreprint
Bıyık, E., Huynh, N., Kochenderfer, M. J., & Sadigh, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02575 · Cited by 7 · see citing works
Real-time bidding with multi-agent reinforcement learning in multi-channel display advertising
Chen, C., Wang, G., Liu, B., Song, S., Mao, K., Yu, S., & Liu, J. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10649-6 · Cited by 5 · see citing works
Advances in Preference-based Reinforcement Learning: A Review
Abdelkareem, Y., Shehata, S., & Karray, F. O. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945333 · Cited by 5 · see citing works
Approximately-Optimal Queries for Planning in Reward-Uncertain Markov Decision Processes
Zhang, S., Durfee, E. H., & Singh, S. P. (2017) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v27i1.13833 · Cited by 5 · see citing works
Personalization in Human-Robot Interaction Through Preference-Based Action Representation Learning
Wang, R., Zhao, D., Suh, D., Yuan, Z., Chen, G., & Min, B. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128756 · Cited by 4 · see citing works
A Policy Iteration Algorithm for Learning from Preference-Based Feedback
Wirth, C., & Fürnkranz, J. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41398-8_37 · Cited by 4 · see citing works
Model-Free Reinforcement Learning with Skew-Symmetric Bilinear Utilities
Gilbert, H., Zanuttini, B., Viappiani, P., Weng, P., & Nicart, E. (2016) · HAL · Cited by 4 · see citing works
Balancing Information with Observation Costs in Deep Reinforcement Learning
Bellinger, C., Drozdyuk, A., Crowley, M., & Tamblyn, I. (2022) · doi:10.21428/594757db.8e09102d · Cited by 4 · see citing works
Feith, N., & Rueckert, E. (2024) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur61395.2024.10597501 · Cited by 4 · see citing works
SEQUEL: Semi-Supervised Preference-based RL with Query Synthesis via Latent Interpolation
Marta, D., Holk, S., Pek, C., & Leite, I. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610534 · Cited by 4 · see citing works
Chen, V., Williams, J., Leben, D., & Heidari, H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i5.25737 · Cited by 3 · see citing works
Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning
Hong, M.-H., Sunberg, Z. N., & Szafir, D. A. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642903 · Cited by 3 · see citing works
Style-Based Reinforcement Learning: Task Decoupling Personalization for Human-Robot Collaboration
Bonyani, M., Soleymani, M., & Wang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60875-9_13 · Cited by 3 · see citing works
Reinforcing an Image Caption Generator Using Off-Line Human Feedback
Seo, P. H., Sharma, P. K., Levinboim, T., Han, B., & Soricut, R. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5655 · Cited by 3 · see citing works
Active Inverse Learning in Stackelberg Trajectory Games
Ward, W., Yu, Y., Levy, J., Mehr, N., Fridovich-Keil, D., & Topcu, U. (2025) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc63710.2025.11107834 · Cited by 2 · see citing works
Learning Human-Robot Handshaking Preferences for Quadruped Robots
Chappuis, A., Bellegarda, G., & Ijspeert, A. J. (2024) · IEEE RO-MAN · doi:10.1109/ro-man60168.2024.10731277 · Cited by 2 · see citing works
Interactive Reward Tuning: Interactive Visualization for Preference Elicitation
Shi, D., Zhu, S., Weinkauf, T., & Oulasvirta, A. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801540 · Cited by 2 · see citing works
Information Directed Reward Learning for Reinforcement LearningPreprint
Lindner, D., Turchetta, M., Tschiatschek, S., Ciosek, K., & Krause, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.12466 · Cited by 2 · see citing works
Lyu, H., Bai, Y., Liang, X. T., Das, U., Shi, C., Gong, L., Li, Y., Sun, M., Ge, M., & Ma, X. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645145 · Cited by 1 · see citing works
Medical Knowledge Integration Into Reinforcement Learning Algorithms for Dynamic Treatment Regimes
Yazzourh, S., Savy, N., Saint-Pierre, P., & Kosorok, M. R. (2025) · International Statistical Review · doi:10.1111/insr.12617 · Cited by 2 · see citing works
An Enhanced Preference-Based Reinforcement Learning Framework for Autonomous System
Ding, D., Kabir, R., & Nnadi, L. C. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8889-0_1 · Cited by 1 · see citing works
Learning from Trajectory-Based Action Preferences
Wirth, C., & Fürnkranz, J. (2013) · Cited by 1 · see citing works
Using feedback in adaptive and user-dependent one-step decision making
Karami, A., & Fleury, A. (2016) · HAL · Cited by 1 · see citing works
Sequential Decision Making Under Uncertainty Using Ordinal Preferential Information
Gilbert, H. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23114-3_36 · Cited by 1 · see citing works
Treatment Recommendation with Preference-based Reinforcement Learning
Xu, N., Kamra, N., & Liu, Y. (2021) · IEEE International Conference on Big Knowledge (ICBK) · doi:10.1109/ickg52313.2021.00025 · Cited by 1 · see citing works
Smooth Convex Optimization Using Sub-Zeroth-Order Oracles
Karabag, M. O., Neary, C., & Topcu, U. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16499
From Preference-Based to Multiobjective Sequential Decision-Making
Weng, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49397-8_20
Autonomous animation of humanoid robots
Tay, J. (2016) · doi:10.32657/10356/69279
Learning individual motion preferences from audience feedback of motion sequences
Tay, J., Veloso, M., & Chen, I. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989322
Context-aware Active Multi-Step Reinforcement LearningPreprint
Chen, G., Li, D., & Xu, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.04107 · Cited by 1 · see citing works
Specifying User Preferences for Autonomous Robots through Interactive Learning
Wilde, N. (2020) · UWSpace (University of Waterloo)
Mobile Health Technologies for Ambient Assisted Living and Healthcare
Pires, I. M. (2023) · MDPI eBooks · doi:10.3390/books978-3-0365-7027-3
Inverse Reinforcement Learning with Learning and Leveraging Demonstrators’ Varying Expertise Levels
Oguchienti, S. M., & Ghasemi, M. (2023) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton58177.2023.10313475
Optimizing Personalized Robot Actions with Ranking of Trajectories
Huang, H. M., Liu, Y., Yuan, S., Wen, C., Hao, Y., & Fang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78110-0_1
Deep reinforcement learning in healthcare and biomedical application
Thakre, B. P., Yadav, U., & Bondre, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30146-9.00008-3 · Cited by 1 · see citing works
Interactive Groupwise Comparison for Reinforcement Learning from Human Feedback
Kompatscher, J., Shi, D., Varni, G., Weinkauf, T., & Oulasvirta, A. (2025) · Computer Graphics Forum · doi:10.1111/cgf.70290
- Authors
- Akrour, R., Schoenauer, M., & Sebag, M.
- Affiliations
- Université Paris-Sud (FR)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 116-131
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1583953806
- Cited by
- 95
- References
- 35
- Citations by year
- 2025: 6 · 2024: 15 · 2023: 8 · 2022: 8 · 2021: 13 · 2020: 11 · 2019: 6 · 2018: 5 · 2017: 4 · 2016: 8 · 2015: 1 · 2014: 4 · 2013: 6
- FWCI
- 2.70
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- preference-based reinforcement learning, active learning (machine learning), expert preferences, swarm robotics, ranking queries, mountain car
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
