| Learning policies with zero or bounded constraint violation for constrained mdps T Liu*, R Zhou*, D Kalathil, P Kumar, C Tian Advances in Neural Information Processing Systems 34, 17183-17193, 2021 | 67 | 2021 |
| Policy Optimization for Constrained MDPs with Provable Fast Global Convergence T Liu*, R Zhou*, D Kalathil, PR Kumar, C Tian arXiv preprint arXiv:2111.00552, 2021 | 20* | 2021 |
| Anchor-changing regularized natural policy gradient for multi-objective reinforcement learning R Zhou*, T Liu*, D Kalathil, PR Kumar, C Tian Advances in Neural Information Processing Systems 35, 13584-13596, 2022 | 10 | 2022 |
| Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation R Zhou*, T Liu*, M Cheng, D Kalathil, PR Kumar, C Tian Advances in Neural Information Processing Systems 36, 2023 | 4 | 2023 |
| Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales T Liu, PR Kumar, R Zhou, X Liu Advances in Neural Information Processing Systems 35, 9151-9163, 2022 | 4 | 2022 |
| Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games Y Sun*, T Liu*, R Zhou, PR Kumar, S Shahrampour Advances in Neural Information Processing Systems 36, 2023 | 3 | 2023 |
| An Optimized Low-Power Optical Memory Access Network for Kilocore Systems T Liu, H Gu, Y Wang, W Zou IEICE TRANSACTIONS on Information and Systems 102 (5), 1085-1088, 2019 | 2 | 2019 |
| Learning a Constrained Optimizer: A Primal Method T Liu, A Cherian AAAI CPML Bridge Workshop, 2023 | 1 | 2023 |
P. R. KumarTexas A&M UniversityVerified email at tamu.edu
