Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning R Das, S Dhuliawala, M Zaheer, L Vilnis, I Durugkar, A Krishnamurthy, ... arXiv preprint arXiv:1711.05851, 2017 | 565 | 2017 |
Generative Multi-Adversarial Networks I Durugkar, I Gemp, S Mahadevan International Conference on Learning Representations, 2017, 2017 | 455 | 2017 |
Cohort intelligence: a self supervised learning behavior AJ Kulkarni, IP Durugkar, M Kumar 2013 IEEE international conference on systems, man, and cybernetics, 1396-1400, 2013 | 132 | 2013 |
Predictive off-policy policy evaluation for nonstationary decision problems, with applications to digital marketing P Thomas, G Theocharous, M Ghavamzadeh, I Durugkar, E Brunskill Proceedings of the AAAI Conference on Artificial Intelligence 31 (2), 4740-4745, 2017 | 64 | 2017 |
An imitation from observation approach to transfer learning with dynamics mismatch S Desai, I Durugkar, H Karnan, G Warnell, J Hanna, P Stone Advances in Neural Information Processing Systems 33, 3917-3929, 2020 | 43 | 2020 |
Adversarial intrinsic motivation for reinforcement learning I Durugkar, M Tec, S Niekum, P Stone Advances in Neural Information Processing Systems 34, 8622-8636, 2021 | 30 | 2021 |
Deep reinforcement learning with macro-actions IP Durugkar, C Rosenbaum, S Dernbach, S Mahadevan arXiv preprint arXiv:1606.04615, 2016 | 27 | 2016 |
Balancing individual preferences and shared objectives in multiagent reinforcement learning I Durugkar, E Liebman, P Stone International Joint Conference on Artificial Intelligence, 2020 | 20 | 2020 |
Reducing sampling error in batch temporal difference learning B Pavse, I Durugkar, J Hanna, P Stone International Conference on Machine Learning, 7543-7552, 2020 | 14 | 2020 |
TD learning with constrained gradients I Durugkar, P Stone | 14 | 2018 |
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 | 6 | 2022 |
Wasserstein distance maximizing intrinsic control I Durugkar, S Hansen, S Spencer, V Mnih arXiv preprint arXiv:2110.15331, 2021 | 4 | 2021 |
Unmixing in the presence of nuisances with deep generative models M Parente, I Gemp, I Durugkar 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS …, 2017 | 4 | 2017 |
An imitation from observation approach to sim-to-real transfer S Desai, I Durugkar, H Karnan, G Warnell, J Hanna, P Stone, A Sony 2nd Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics. RSS, 2020 | 3 | 2020 |
ABC: Adversarial Behavioral Cloning for Offline Mode-Seeking Imitation Learning E Hudson, I Durugkar, G Warnell, P Stone arXiv preprint arXiv:2211.04005, 2022 | 2 | 2022 |
DM : Distributed multi-agent reinforcement learning via distribution matching C Wang | 2 | 2022 |
Multi-preference actor critic I Durugkar, M Hausknecht, A Swaminathan, P MacAlpine arXiv preprint arXiv:1904.03295, 2019 | 2 | 2019 |
Inverting variational autoencoders for improved generative accuracy I Gemp, I Durugkar, M Parente, MD Dyar, S Mahadevan arXiv preprint arXiv:1608.05983, 2016 | 2 | 2016 |
f-Policy Gradients: A General Framework for Goal-Conditioned RL using f-Divergences S Agarwal, I Durugkar, P Stone, A Zhang Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
Estimation and control of visitation distributions for reinforcement learning I Durugkar | 1 | 2023 |