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Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning.

Z. Xu, K. Panaganti, and D. Kalathil. AISTATS, volume 206 of Proceedings of Machine Learning Research, page 9728-9754. PMLR, (2023)

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Sample Complexity of Model-Based Robust Reinforcement Learning.K. Panaganti, and D. Kalathil. CDC, page 2240-2245. IEEE, (2021)Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning.R. Zhou, T. Liu, D. Kalathil, P. Kumar, and C. Tian. NeurIPS, (2022)AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning.V. Gohil, S. Patnaik, D. Kalathil, and J. Rajendran. USENIX Security Symposium, USENIX Association, (2024)Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems.T. Chang, S. Chaudhary, D. Kalathil, and S. Shahrampour. Trans. Mach. Learn. Res., (2023)Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning.Z. Xu, K. Panaganti, and D. Kalathil. AISTATS, volume 206 of Proceedings of Machine Learning Research, page 9728-9754. PMLR, (2023)Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees.K. Badrinath, and D. Kalathil. ICML, volume 139 of Proceedings of Machine Learning Research, page 511-520. PMLR, (2021)Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments.D. Rengarajan, S. Chaudhary, J. Kim, D. Kalathil, and S. Shakkottai. CoRR, (2022)DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning.A. Bura, A. HasanzadeZonuzy, D. Kalathil, S. Shakkottai, and J. Chamberland. NeurIPS, (2022)Robust Reinforcement Learning using Offline Data.K. Panaganti, Z. Xu, D. Kalathil, and M. Ghavamzadeh. NeurIPS, (2022)DETERRENT: detecting trojans using reinforcement learning.V. Gohil, S. Patnaik, H. Guo, D. Kalathil, and J. Rajendran. DAC, page 697-702. ACM, (2022)

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Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning.

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Disambiguation of "Kalathil, Dileep"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning.

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Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning.