Multi-agent incentive communication via decentralized teammate modeling L Yuan, J Wang, F Zhang, C Wang, Z Zhang, Y Yu, C Zhang Proceedings of the AAAI Conference on Artificial Intelligence 36 (9), 9466-9474, 2022 | 36 | 2022 |
Discovering generalizable multi-agent coordination skills from multi-task offline data F Zhang, C Jia, YC Li, L Yuan, Y Yu, Z Zhang The Eleventh International Conference on Learning Representations, 2022 | 19 | 2022 |
Multi-Agent Concentrative Coordination with Decentralized Task Representation. L Yuan, C Wang, J Wang, F Zhang, F Chen, C Guan, Z Zhang, C Zhang, ... IJCAI, 599-605, 2022 | 13 | 2022 |
Policy regularization with dataset constraint for offline reinforcement learning Y Ran, YC Li, F Zhang, Z Zhang, Y Yu International Conference on Machine Learning, 28701-28717, 2023 | 7 | 2023 |
Model gradient: unified model and policy learning in model-based reinforcement learning C Jia, F Zhang, T Xu, JC Pang, Z Zhang, Y Yu Frontiers of Computer Science 18 (4), 184339, 2024 | 2 | 2024 |
Multi-agent Continual Coordination via Progressive Task Contextualization L Yuan, L Li, Z Zhang, F Zhang, C Guan, Y Yu arXiv preprint arXiv:2305.13937, 2023 | 1 | 2023 |
Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation C Jia, F Zhang, YC Li, CX Gao, XH Liu, L Yuan, Z Zhang, Y Yu arXiv preprint arXiv:2403.07261, 2024 | | 2024 |
Internal Logical Induction for Pixel-Symbolic Reinforcement Learning J Xu, C Chen, F Zhang, L Yuan, Z Zhang, Y Yu Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | | 2023 |
Towards deployment-efficient and collision-free multi-agent path finding (student abstract) F Chen, C Wang, F Zhang, H Ding, Q Zhong, S Pu, Z Zhang Proceedings of the AAAI Conference on Artificial Intelligence 37 (13), 16182 …, 2023 | | 2023 |
Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning C Jia, C Gao, H Yin, F Zhang, XH Chen, T Xu, L Yuan, Z Zhang, ZH Zhou, ... The Twelfth International Conference on Learning Representations, 0 | | |