| Bridging distributional and risk-sensitive reinforcement learning with provable regret bounds H Liang, ZQ Luo arXiv preprint arXiv:2210.14051, 2022 | 6 | 2022 |
| Regret bounds for risk-sensitive reinforcement learning with lipschitz dynamic risk measures H Liang, Z Luo International Conference on Artificial Intelligence and Statistics, 1774-1782, 2024 | 3 | 2024 |
| A distribution optimization framework for confidence bounds of risk measures H Liang, Z Luo International Conference on Machine Learning, 20677-20705, 2023 | 1 | 2023 |
| Optimistic Thompson Sampling for No-Regret Learning in Unknown Games Y Li, L Liu, W Pu, H Liang, ZQ Luo arXiv preprint arXiv:2402.09456, 2024 | | 2024 |
| Model-based Distributional Reinforcement Learning for Risk-sensitive Control H Liang, ZQ Luo NeurIPS 2021 Ecological Theory of RL Workshop, 2021 | | 2021 |
| Efficient Exploration by HyperDQN in Deep Reinforcement Learning Z Li, Y Li, H Liang, T Zhang Reinforcement Learning for Real Life Workshop @ ICML 2021, 2021 | | 2021 |
Zhi-Quan LuoProfessor, The Chinese University of Hong Kong, Shenzhen, China在 cuhk.edu.cn 的电子邮件经过验证
