Follow
Mingxiao Feng
Mingxiao Feng
Verified email at mail.ustc.edu.cn
Title
Cited by
Cited by
Year
Playvirtual: Augmenting cycle-consistent virtual trajectories for reinforcement learning
T Yu, C Lan, W Zeng, M Feng, Z Zhang, Z Chen
Advances in Neural Information Processing Systems 34, 5276-5289, 2021
202021
H-tsp: Hierarchically solving the large-scale traveling salesman problem
X Pan, Y Jin, Y Ding, M Feng, L Zhao, L Song, J Bian
Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 9345-9353, 2023
152023
Multi-agent reinforcement learning with shared resources for inventory management
Y Ding, M Feng, G Liu, W Jiang, C Zhang, L Zhao, L Song, H Li, Y Jin, ...
arXiv preprint arXiv:2212.07684, 2022
82022
Stabilizing voltage in power distribution networks via multi-agent reinforcement learning with transformer
M Wang, M Feng, W Zhou, H Li
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022
52022
MA2CL: Masked attentive contrastive learning for multi-agent reinforcement learning
H Song, M Feng, W Zhou, H Li
arXiv preprint arXiv:2306.02006, 2023
32023
Boosting Multi-Agent Reinforcement Learning via Transition-Informed Representations
M Feng, W Zhou, Y Yang, H Li
2023
Multi-Agent Hierarchical Graph Attention Reinforcement Learning for Grid-Aware Energy Management
B FENG, M FENG, M WANG, W ZHOU, H LI
ZTE Communications 21 (3), 11, 2023
2023
Sample Efficient Reinforcement Learning with Double Importance Sampling Weight Clipping
J Han, M Feng, W Zhou, H Li
2023 IEEE Conference on Games (CoG), 1-8, 2023
2023
Multi-Agent Reinforcement Learning with Safety Layer for Active Voltage Control
Y Shi, M Feng, M Wang, W Zhou, H Li
Proceedings of the 2023 International Conference on Autonomous Agents and …, 2023
2023
Joint-Predictive Representations for Multi-Agent Reinforcement Learning
M Feng, W Zhou, Y Yang, H Li
2022
Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management
M Feng, G Liu, L Zhao, L Song, J Bian, T Qin, W Zhou, H Li, TY Liu
2021
Timar: Transition-Informed Representation for Sample-Efficient Multi-Agent Reinforcement Learning
M Feng, Y Yang, W Zhou, H Li
Available at SSRN 4706110, 0
The system can't perform the operation now. Try again later.
Articles 1–12