6 papers
Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
Yuheng Jing, Kai Li, Ziwen Zhang +8
In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT)-where coordination with un…
K^2-Agent: Co-Evolving Know-What and Know-How for Hierarchical Mobile Device Control
Zhe Wu, Donglin Mo, Hongjin Lu +7
Existing mobile device control agents often perform poorly when solving complex tasks requiring long-horizon planning and precise operations, typically due to a lack of relevant ta…
Hi-Agent: Hierarchical Vision-Language Agents for Mobile Device Control
Zhe Wu, Hongjin Lu, Junliang Xing +10
Building agents that autonomously operate mobile devices has attracted increasing attention. While Vision-Language Models (VLMs) show promise, most existing approaches rely on dire…
BodyGen: Advancing Towards Efficient Embodiment Co-Design
Haofei Lu, Zhe Wu, Junliang Xing +4
Embodiment co-design aims to optimize a robot's morphology and control policy simultaneously. While prior work has demonstrated its potential for generating environment-adaptive ro…
Transductive Off-policy Proximal Policy Optimization
Yaozhong Gan, Renye Yan, Xiaoyang Tan +2
Proximal Policy Optimization (PPO) is a popular model-free reinforcement learning algorithm, esteemed for its simplicity and efficacy. However, due to its inherent on-policy nature…
Reflective Policy Optimization
Yaozhong Gan, Renye Yan, Zhe Wu +1
On-policy reinforcement learning methods, like Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), often demand extensive data per update, leading to sa…