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Jakub Grudzien Kuba
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2020 – today
- 2024
- [j2]Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang:
Heterogeneous-Agent Reinforcement Learning. J. Mach. Learn. Res. 25: 32:1-32:67 (2024) - [i11]Jakub Grudzien Kuba, Masatoshi Uehara, Pieter Abbeel, Sergey Levine:
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization. CoRR abs/2401.05442 (2024) - 2023
- [j1]Shangding Gu
, Jakub Grudzien Kuba, Yuanpei Chen, Yali Du, Long Yang, Alois C. Knoll, Yaodong Yang
Safe multi-agent reinforcement learning for multi-robot control. Artif. Intell. 319: 103905 (2023) - [i10]Yifan Zhong, Jakub Grudzien Kuba, Siyi Hu, Jiaming Ji, Yaodong Yang
Heterogeneous-Agent Reinforcement Learning. CoRR abs/2304.09870 (2023) - [i9]Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner, Jakub Grudzien Kuba, Sergey Levine:
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies. CoRR abs/2304.10573 (2023) - 2022
- [c5]Jakub Grudzien Kuba, Ruiqing Chen, Muning Wen, Ying Wen, Fanglei Sun, Jun Wang, Yaodong Yang:
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning. ICLR 2022 - [c4]Jakub Grudzien Kuba, Christian A. Schröder de Witt, Jakob N. Foerster:
Mirror Learning: A Unifying Framework of Policy Optimisation. ICML 2022: 7825-7844 - [c3]Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schröder de Witt, Jakob N. Foerster:
Discovered Policy Optimisation. NeurIPS 2022 - [c2]Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang:
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem. NeurIPS 2022 - [i8]Jakub Grudzien Kuba, Christian Schröder de Witt, Jakob N. Foerster:
Mirror Learning: A Unifying Framework of Policy Optimisation. CoRR abs/2201.02373 (2022) - [i7]Zehao Dou, Jakub Grudzien Kuba, Yaodong Yang:
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2202.04868 (2022) - [i6]Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem. CoRR abs/2205.14953 (2022) - [i5]Jakub Grudzien Kuba, Xidong Feng, Shiyao Ding, Hao Dong, Jun Wang, Yaodong Yang
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL. CoRR abs/2208.01682 (2022) - [i4]Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schröder de Witt, Jakob N. Foerster:
Discovered Policy Optimisation. CoRR abs/2210.05639 (2022) - 2021
- [c1]Jakub Grudzien Kuba, Muning Wen, Linghui Meng, Shangding Gu, Haifeng Zhang, David Mguni, Jun Wang, Yaodong Yang:
Settling the Variance of Multi-Agent Policy Gradients. NeurIPS 2021: 13458-13470 - [i3]Jakub Grudzien Kuba, Muning Wen, Yaodong Yang, Linghui Meng, Shangding Gu, Haifeng Zhang, David Henry Mguni, Jun Wang:
Settling the Variance of Multi-Agent Policy Gradients. CoRR abs/2108.08612 (2021) - [i2]Jakub Grudzien Kuba, Ruiqing Chen, Muning Wen, Ying Wen, Fanglei Sun, Jun Wang, Yaodong Yang:
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning. CoRR abs/2109.11251 (2021) - [i1]Shangding Gu, Jakub Grudzien Kuba, Muning Wen, Ruiqing Chen, Ziyan Wang, Zheng Tian, Jun Wang, Alois C. Knoll, Yaodong Yang:
Multi-Agent Constrained Policy Optimisation. CoRR abs/2110.02793 (2021)
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