42 citations · 78 across the 9 of their papers we have counts for
9 papers
Enhancing Decision-Making of Large Language Models via Actor-Critic
Heng Dong, Kefei Duan, Chongjie Zhang
Large Language Models (LLMs) have achieved remarkable advancements in natural language processing tasks, yet they encounter challenges in complex decision-making scenarios that req…
Translate Policy to Language: Flow Matching Generated Rewards for LLM Explanations
Xinyi Yang, Liang Zeng, Heng Dong +6
As humans increasingly share environments with diverse agents powered by RL, LLMs, and beyond, the ability to explain agent policies in natural language is vital for reliable coexi…
On Diffusion Models for Multi-Agent Partial Observability: Shared Attractors, Error Bounds, and Composite Flow
Tonghan Wang, Heng Dong, Yanchen Jiang +2
Multiagent systems grapple with partial observability (PO), and the decentralized POMDP (Dec-POMDP) model highlights the fundamental nature of this challenge. Whereas recent approa…
Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design
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. Previous research has demonstrated the ab…
Symmetry-Aware Robot Design with Structured Subgroups
Heng Dong, Junyu Zhang, Tonghan Wang +1
Robot design aims at learning to create robots that can be easily controlled and perform tasks efficiently. Previous works on robot design have proven its ability to generate robot…
Low-Rank Modular Reinforcement Learning via Muscle Synergy
Heng Dong, Tonghan Wang, Jiayuan Liu +1
Modular Reinforcement Learning (RL) decentralizes the control of multi-joint robots by learning policies for each actuator. Previous work on modular RL has proven its ability to co…
Birds of a Feather Flock Together: A Close Look at Cooperation Emergence via Multi-Agent RL
Heng Dong, Tonghan Wang, Jiayuan Liu +2
How cooperation emerges is a long-standing and interdisciplinary problem. Game-theoretical studies on social dilemmas reveal that altruistic incentives are critical to the emergenc…
Off-Policy Multi-Agent Decomposed Policy Gradients
Yihan Wang, Beining Han, Tonghan Wang +2
Multi-agent policy gradient (MAPG) methods recently witness vigorous progress. However, there is a significant performance discrepancy between MAPG methods and state-of-the-art mul…
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
Tonghan Wang, Heng Dong, Victor Lesser +1
The role concept provides a useful tool to design and understand complex multi-agent systems, which allows agents with a similar role to share similar behaviors. However, existing…