| Decentralised learning in systems with many, many strategic agents D Mguni, J Jennings, EM de Cote Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 82 | 2018 |
| Multi-agent determinantal q-learning Y Yang, Y Wen, J Wang, L Chen, K Shao, D Mguni, W Zhang ICML 2020, 10757-10766, 2020 | 81 | 2020 |
| SAUTE RL: Almost Surely Safe Reinforcement Learning Using State Augmentation A Sootla, AI Cowen-Rivers, T Jafferjee, Z Wang, D Mguni, J Wang, ... ICML, 2022 | 70 | 2022 |
| Modelling behavioural diversity for learning in open-ended games NP Nieves, Y Yang, O Slumbers, DH Mguni, Y Wen, J Wang ICML 2021, Long Oral, 2021 | 67* | 2021 |
| Settling the variance of multi-agent policy gradients JG Kuba, M Wen, L Meng, H Zhang, D Mguni, J Wang, Y Yang Advances in Neural Information Processing Systems 34, 13458-13470, 2021 | 62 | 2021 |
| Learning in Nonzero-Sum Stochastic Games with Potentials D Mguni, Y Wu, Y Du, Y Yang, Z Wang, M Li, Y Wen, J Jennings, J Wang ICML 2021 139, 7688--7699, 2021 | 59* | 2021 |
| Coordinating the crowd: Inducing desirable equilibria in non-cooperative systems D Mguni, J Jennings, SV Macua, E Sison, S Ceppi, EM De Cote AAMAS 2019, 386–394, 2019 | 54 | 2019 |
| On the complexity of computing markov perfect equilibrium in general-sum stochastic games X Deng, Y Li, DH Mguni, J Wang, Y Yang National Science Review, 2095-2138, 2021 | 48 | 2021 |
| Online double oracle LC Dinh, Y Yang, S McAleer, Z Tian, NP Nieves, O Slumbers, DH Mguni, ... Transactions on Machine Learning Research, 2023 | 33 | 2023 |
| ChessGPT: Bridging Policy Learning and Language Modeling X Feng, Y Luo, Z Wang, H Tang, M Yang, K Shao, D Mguni, Y Du, J Wang NeurIPS 2023, 2023 | 32 | 2023 |
| LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning DH Mguni, T Jafferjee, J Wang, N Perez-Nieves, O Slumbers, F Tong, Y Li, ... ICLR, 2022 | 22 | 2022 |
| A game-theoretic framework for managing risk in multi-agent systems O Slumbers, DH Mguni, SB Blumberg, SM Mcaleer, Y Yang, J Wang International Conference on Machine Learning, 32059-32087, 2023 | 16* | 2023 |
| A viscosity approach to stochastic differential games of control and stopping involving impulsive control D Mguni arXiv preprint arXiv:1803.11432, 2018 | 14 | 2018 |
| Socially-Attentive Policy Optimization in Multi-Agent Self-Driving System Z Dai, T Zhou, K Shao, DH Mguni, B Wang, HAO Jianye 6th Annual Conference on Robot Learning, 2022 | 11 | 2022 |
| Learning to Shape Rewards using a Game of Two Partners D Mguni, T Jafferjee, J Wang, N Perez-Nieves, W Song, F Tong, M Taylor, ... AAAI 2023, 2023 | 10* | 2023 |
| Cutting your losses: Learning fault-tolerant control and optimal stopping under adverse risk D Mguni arXiv preprint arXiv:1902.05045, 2019 | 10 | 2019 |
| Incentive control for multi-agent systems D Mguni, S Ceppi, S Macua, EM DE COTE US Patent App. 17/261,500, 2021 | 8 | 2021 |
| Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints D Mguni, A Sootla, J Ziomek, O Slumbers, Z Dai, K Shao, J Wang ICLR 2023, 2023 | 7 | 2023 |
| A survey on algorithms for Nash equilibria in finite normal-form games H Li, W Huang, Z Duan, DH Mguni, K Shao, J Wang, X Deng Computer Science Review, 2024 | 6 | 2024 |
| Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models X Yan, Y Song, X Cui, F Christianos, H Zhang, DH Mguni, J Wang arXiv preprint arXiv:2310.18127, 2023 | 6 | 2023 |
Jun WangProfessor, Computer Science, University College LondonVerified email at cs.ucl.ac.uk
