| Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey S Narvekar, B Peng, M Leonetti, J Sinapov, ME Taylor, P Stone Journal of Machine Learning Research 21 (121), 1-50, 2020 | 870 | 2020 |
| Recsim: A configurable simulation platform for recommender systems E Ie, C Hsu, M Mladenov, V Jain, S Narvekar, J Wang, R Wu, C Boutilier arXiv preprint arXiv:1909.04847, 2019 | 239 | 2019 |
| Source task creation for curriculum learning S Narvekar, J Sinapov, M Leonetti, P Stone Proceedings of the 2016 international conference on autonomous agents …, 2016 | 184 | 2016 |
| SlateQ: A tractable decomposition for reinforcement learning with recommendation sets E Ie, V Jain, J Wang, S Narvekar, R Agarwal, R Wu, HT Cheng, T Chandra, ... International Joint Conference on Artificial Intelligence (IJCAI), 2019 | 181 | 2019 |
| Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning. S Narvekar, J Sinapov, P Stone International Joint Conference on Artificial Intelligence (IJCAI), 2536-2542, 2017 | 172 | 2017 |
| Learning curriculum policies for reinforcement learning S Narvekar, P Stone Proceedings of the 18th International Conference on Autonomous Agents and …, 2019 | 146 | 2019 |
| Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology E Ie, V Jain, J Wang, S Narvekar, R Agarwal, R Wu, HT Cheng, ... arXiv preprint arXiv:1905.12767, 2019 | 85 | 2019 |
| Learning inter-task transferability in the absence of target task samples J Sinapov, S Narvekar, M Leonetti, P Stone Proceedings of the 2015 international conference on autonomous agents and …, 2015 | 60 | 2015 |
| Fast and precise black and white ball detection for robocup soccer J Menashe, J Kelle, K Genter, J Hanna, E Liebman, S Narvekar, R Zhang, ... Robot world cup, 45-58, 2017 | 35 | 2017 |
| Curriculum Learning in Reinforcement Learning. S Narvekar IJCAI, 5195-5196, 2017 | 33 | 2017 |
| Generalizing Curricula for Reinforcement Learning S Narvekar, P Stone 4th Lifelong Learning Workshop at ICML 2020, 2020 | 11 | 2020 |
| Capturing skill state in curriculum learning for human skill acquisition K Ghonasgi, R Mirsky, S Narvekar, B Masetty, AM Haith, P Stone, ... 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 10 | 2021 |
| Towards a real-time, low-resource, end-to-end object detection pipeline for robot soccer SK Narayanaswami, M Tec, I Durugkar, S Desai, B Masetty, S Narvekar, ... Robot World Cup, 62-74, 2022 | 8 | 2022 |
| UT Austin Villa: project-driven research in AI and robotics K Genter, P MacAlpine, J Menashe, J Hannah, E Liebman, S Narvekar, ... IEEE Intelligent Systems 31 (2), 94-101, 2016 | 7 | 2016 |
| Curriculum Learning in Reinforcement Learning:(Doctoral Consortium). S Narvekar AAMAS, 1528-1529, 2016 | 5 | 2016 |
| Leveraging Reinforcement Learning for Human Motor Skill Acquisition K Ghonasgi, R Mirsky, B Masetty, S Narvekar, A Haith, P Stone, ... Social AI for Human-Robot Interactions of Human-Care Service Robots Workshop …, 2020 | 4 | 2020 |
| Curriculum learning in reinforcement learning SS Narvekar The University of Texas at Austin, 2021 | 1 | 2021 |
| Systems and methods for simulating a complex reinforcement learning environment TWE Ie, SS Narvekar, CE Boutilier US Patent 12,406,205, 2025 | | 2025 |
| Systems and methods for simulating a complex reinforcement learning environment TWE Ie, SS Narvekar, CE Boutilier US Patent 11,475,355, 2022 | | 2022 |
| Constrained Parametric Min-Cuts for Automatic Object Segmentation S Narvekar | | 2012 |
Peter StoneProfessor of Computer Science, The University of Texas at AustinVerified email at cs.utexas.edu
