| Parametrized deep q-networks learning: Reinforcement learning with discrete-continuous hybrid action space J Xiong, Q Wang, Z Yang, P Sun, L Han, Y Zheng, H Fu, T Zhang, J Liu, ... arXiv preprint arXiv:1810.06394, 2018 | 151 | 2018 |
| Finding robust solutions to dynamic optimization problems H Fu, B Sendhoff, K Tang, X Yao Applications of Evolutionary Computation: 16th European Conference …, 2013 | 55 | 2013 |
| Robust optimization over time: Problem difficulties and benchmark problems H Fu, B Sendhoff, K Tang, X Yao IEEE Transactions on Evolutionary Computation 19 (5), 731-745, 2014 | 54 | 2014 |
| What are dynamic optimization problems? H Fu, PR Lewis, B Sendhoff, K Tang, X Yao 2014 IEEE Congress on Evolutionary Computation (CEC), 1550-1557, 2014 | 34 | 2014 |
| Characterizing environmental changes in robust optimization over time H Fu, B Sendhoff, K Tang, X Yao 2012 IEEE Congress on Evolutionary Computation, 1-8, 2012 | 27 | 2012 |
| Find robust solutions over time by two-layer multi-objective optimization method Y Guo, M Chen, H Fu, Y Liu 2014 IEEE Congress on Evolutionary Computation (CEC), 1528-1535, 2014 | 22 | 2014 |
| L2E: Learning to exploit your opponent Z Wu, K Li, H Xu, Y Zang, B An, J Xing 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 18 | 2022 |
| Actor-critic policy optimization in a large-scale imperfect-information game H Fu, W Liu, S Wu, Y Wang, T Yang, K Li, J Xing, B Li, B Ma, Q Fu, Y Wei International Conference on Learning Representations, 2021 | 18 | 2021 |
| Memetic algorithm with heuristic candidate list strategy for capacitated arc routing problem H Fu, Y Mei, K Tang, Y Zhu IEEE Congress on Evolutionary Computation, 1-8, 2010 | 17 | 2010 |
| Quality-Similar Diversity via Population Based Reinforcement Learning S Wu, J Yao, H Fu, Y Tian, C Qian, Y Yang, Q Fu, Y Wei The Eleventh International Conference on Learning Representations, 2022 | 8 | 2022 |
| Greedy when Sure and Conservative when Uncertain about the Opponents H Fu, Y Tian, H Yu, W Liu, S Wu, J Xiong, Y Wen, K Li, J Xing, Q Fu, ... International Conference on Machine Learning, 6829-6848, 2022 | 6 | 2022 |
| A Q-learning based evolutionary algorithm for sequential decision making problems H Fu, PR Lewis, X Yao Parallel Problem Solving from Nature (PPSN). VUB AI Lab, 2014 | 4 | 2014 |
| Curriculum-based Co-design of Morphology and Control of Voxel-based Soft Robots Y Wang, S Wu, H Fu, Q Fu, T Zhang, Y Chang, X Wang The Eleventh International Conference on Learning Representations, 2022 | 3 | 2022 |
| Combining Tree Search and Action Prediction for State-of-the-Art Performance in DouDiZhu. Y Zhang, D Yan, B Shi, H Fu, Q Fu, H Su, J Zhu, N Chen IJCAI, 3413-3419, 2021 | 3 | 2021 |
| Opponent-limited online search for imperfect information games W Liu, H Fu, Q Fu, Y Wei International Conference on Machine Learning, 21567-21585, 2023 | 1 | 2023 |
| Policy space diversity for non-transitive games J Yao, W Liu, H Fu, Y Yang, S McAleer, Q Fu, W Yang arXiv preprint arXiv:2306.16884, 2023 | 1 | 2023 |
| Sequential Cooperative Multi-Agent Reinforcement Learning Y Zang, J He, K Li, H Fu, Q Fu, J Xing Proceedings of the 2023 International Conference on Autonomous Agents and …, 2023 | 1 | 2023 |
| AutoCFR: Learning to Design Counterfactual Regret Minimization Algorithms H Xu, K Li, H Fu, Q Fu, J Xing Proceedings of the AAAI Conference on Artificial Intelligence 36 (5), 5244-5251, 2022 | 1 | 2022 |
| PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE J Xiong, Q Wang, Z Yang, P Sun, Y Zheng, L Han, H Fu, X Lian, ... | 1 | 2018 |
| Finding robust solutions against environmental changes H Fu University of Birmingham, 2014 | 1 | 2014 |
