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David Mguni
David Mguni
Principal Scientist, Noah's Ark Lab, Huawei
Verified email at huawei.com
Title
Cited by
Cited by
Year
Decentralised learning in systems with many, many strategic agents
D Mguni, J Jennings, EM de Cote
Thirty-second AAAI conference on artificial intelligence, 2018
572018
Multi-agent determinantal q-learning
Y Yang, Y Wen, J Wang, L Chen, K Shao, D Mguni, W Zhang
ICML 2020, 10757-10766, 2020
432020
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
25*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
202019
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
17*2021
Online double oracle
YY Le Cong Dinh, Z Tian, NP Nieves, O Slumbers, DH Mguni, HB Ammar, ...
14*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
102021
On the complexity of computing markov perfect equilibrium in general-sum stochastic games
X Deng, Y Li, DH Mguni, J Wang, Y Yang
arXiv preprint arXiv:2109.01795, 2021
102021
A viscosity approach to stochastic differential games of control and stopping involving impulsive control
D Mguni
arXiv preprint arXiv:1803.11432, 2018
102018
Cutting your losses: Learning fault-tolerant control and optimal stopping under adverse risk
D Mguni
arXiv preprint arXiv:1902.05045, 2019
62019
Optimal selection of transaction costs in a dynamic principal-agent problem
D Mguni
arXiv preprint arXiv:1805.01062, 2018
42018
Duopoly Investment Problems with Minimally Bounded Adjustment Costs
D Mguni
arXiv preprint arXiv:1805.11974, 2018
32018
Optimal capital injections with the risk of ruin: A stochastic differential game of impulse control and stopping approach
D Mguni
arXiv preprint arXiv:1805.01578, 2018
32018
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, 2022
22022
Learning Risk-Averse Equilibria in Multi-Agent Systems
O Slumbers, DH Mguni, S McAleer, J Wang, Y Yang
arXiv preprint arXiv:2205.15434, 2022
12022
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, 2021
12021
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
D Mguni, J Jennings, T Jafferjee, A Sootla, Y Yang, C Yu, U Islam, Z Wang, ...
arXiv preprint arXiv:2110.14468, 2021
12021
Efficient reinforcement dynamic mechanism design
D Mguni, M Tomczak
GAIW: Games, agents and incentives workshops, at AAMAS, Montreal, Canada, 2019
12019
Semi-Centralised Multi-Agent Reinforcement Learning with Policy-Embedded Training
T Jafferjee, J Ziomek, T Yang, Z Dai, J Wang, M Taylor, K Shao, J Wang, ...
arXiv preprint arXiv:2209.01054, 2022
2022
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
arXiv preprint arXiv:2205.15953, 2022
2022
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