Ishan Durugkar
Title
Cited by
Cited by
Year
Generative Multi-Adversarial Networks
I Durugkar, I Gemp, S Mahadevan
International Conference on Learning Representations, 2017, 2017
1852017
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
1292017
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
652013
Predictive off-policy policy evaluation for nonstationary decision problems, with applications to digital marketing
PS Thomas, G Theocharous, M Ghavamzadeh, I Durugkar, E Brunskill
Twenty-Ninth IAAI Conference, 2017
122017
Deep reinforcement learning with macro-actions
IP Durugkar, C Rosenbaum, S Dernbach, S Mahadevan
arXiv preprint arXiv:1606.04615, 2016
92016
TD learning with constrained gradients
I Durugkar, P Stone
42018
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
22017
Inverting Variational Autoencoders for Improved Generative Accuracy
I Gemp, I Durugkar, M Parente, MD Dyar, S Mahadevan
arXiv preprint arXiv:1608.05983, 2016
22016
Adversarial goal generation for intrinsic motivation
I Durugkar, P Stone
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
12018
Multi-Preference Actor Critic
I Durugkar, M Hausknecht, A Swaminathan, P MacAlpine
arXiv preprint arXiv:1904.03295, 2019
2019
HR-TD: A Regularized TD Method to Avoid Over-Generalization
I Durugkar, B Liu, P Stone
2018
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
2017
Deep Generative Models for Spectroscopic Analysis on Mars
I Gemp, I Durugkar, M Parente, S Mahadevan
CoRR, 2016
2016
Balancing Individual Preferences and Shared Objectives in Multiagent Reinforcement Learning
I Durugkar, E Liebman, P Stone
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Articles 1–14