| Deep Reinforcement Learning: Fundamentals, Research, and Applications H Dong, Z Ding, S Zhang, H Yuan, H Zhang, J Zhang, Y Huang, T Yu, ... Springer Singapore, 2020 | 311 | 2020 |
| Taxonomy of reinforcement learning algorithms H Zhang, T Yu Deep reinforcement learning: Fundamentals, research and applications, 125-133, 2020 | 99 | 2020 |
| AlphaZero H Zhang, T Yu Deep Reinforcement Learning: Fundamentals, Research and Applications, 391-415, 2020 | 27 | 2020 |
| Efficient reinforcement learning development with rlzoo Z Ding, T Yu, H Zhang, Y Huang, G Li, Q Guo, L Mai, H Dong Proceedings of the 29th ACM International Conference on Multimedia, 3759-3762, 2021 | 18* | 2021 |
| Picor: Multi-task deep reinforcement learning with policy correction F Bai, H Zhang, T Tao, Z Wu, Y Wang, B Xu Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 6728-6736, 2023 | 16 | 2023 |
| Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay H Zhang, C Xiao, H Wang, J Jin, B Xu, M Müller The Eleventh International Conference on Learning Representations, 2023 | 9 | 2023 |
| Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning H Zhang, T Ren, C Xiao, D Schuurmans, B Dai Forty-first International Conference on Machine Learning, 2024 | 5 | 2024 |
| Combine Deep Q-Networks with Actor-Critic H Zhang, T Yu, R Huang Deep Reinforcement Learning: Fundamentals, Research and Applications, 213-245, 2020 | 4 | 2020 |
| A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning H Zhang, K Sun, B Xu, L Kong, M Müller Transactions on Machine Learning Research, 2024 | 2* | 2024 |
| A logarithmic barrier method for proximal policy optimization C Zeng, H Zhang arXiv preprint arXiv:1812.06502, 2018 | 2 | 2018 |
| Latent Landmark Graph for Efficient Exploration-exploitation Balance in Hierarchical Reinforcement Learning Q Zhang, H Zhang, D Xing, B Xu Machine Intelligence Research, 1-22, 2025 | | 2025 |
| -DQN: Improving Deep Q-Learning By Evolving the Behavior H Zhang, F Bai, C Xiao, C Gao, B Xu, M Müller The 24th International Conference on Autonomous Agents and Multiagent …, 2025 | | 2025 |
| Monte Carlo Tree Search in the Presence of Transition Uncertainty F Kohankhaki, K Aghakasiri, H Zhang, TH Wei, C Gao, M Müller Proceedings of the AAAI Conference on Artificial Intelligence 38 (18), 20151 …, 2024 | | 2024 |
| Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration H Zhang, C Xiao, C Gao, H Wang, X Bo, M Müller The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | | 2024 |
| Build generally reusable agent-environment interaction models J Jin, H Zhang, J Luo arXiv preprint arXiv:2211.08234, 2022 | | 2022 |
| Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning J Long, H Zhang, T Yu, B Xu arXiv preprint arXiv:1908.06758, 2019 | | 2019 |
| RevCuT Tree Search Method in Complex Single-player Game with Continuous Search Space H Zhang, F Cheng, B Xu, F Chen, J Liu, W Wu 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | | 2019 |
Hao DongAssistant Professor at Peking UniversityVerificeret mail på pku.edu.cn