no code implementations • 1 Nov 2023 • Heng Dong, Junyu Zhang, Chongjie Zhang
Multi-cellular robot design aims to create robots comprised of numerous cells that can be efficiently controlled to perform diverse tasks.
1 code implementation • 31 May 2023 • Heng Dong, Junyu Zhang, Tonghan Wang, Chongjie Zhang
Robot design aims at learning to create robots that can be easily controlled and perform tasks efficiently.
1 code implementation • 26 Oct 2022 • Heng Dong, Tonghan Wang, Jiayuan Liu, Chongjie Zhang
Modular Reinforcement Learning (RL) decentralizes the control of multi-joint robots by learning policies for each actuator.
no code implementations • 29 Sep 2021 • Heng Dong, Tonghan Wang, Jiayuan Liu, Chi Han, Chongjie Zhang
Promoting cooperation among self-interested agents is a long-standing and interdisciplinary problem, but receives less attention in multi-agent reinforcement learning (MARL).
no code implementations • 23 Apr 2021 • Heng Dong, Tonghan Wang, Jiayuan Liu, Chi Han, Chongjie Zhang
We propose a novel learning framework to encourage homophilic incentives and show that it achieves stable cooperation in both SSDs of public goods and tragedy of the commons.
no code implementations • ICLR 2021 • Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, Chongjie Zhang
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
1 code implementation • 24 Jul 2020 • Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, Chongjie Zhang
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
1 code implementation • ICML 2020 • Tonghan Wang, Heng Dong, Victor Lesser, Chongjie Zhang
In this paper, we synergize these two paradigms and propose a role-oriented MARL framework (ROMA).
Multiagent Systems