no code implementations • 8 Jun 2024 • Chengyuan Deng, Yiqun Duan, Xin Jin, Heng Chang, Yijun Tian, Han Liu, Henry Peng Zou, Yiqiao Jin, Yijia Xiao, Yichen Wang, Shenghao Wu, Zongxing Xie, Kuofeng Gao, Sihong He, Jun Zhuang, Lu Cheng, Haohan Wang
Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years.
no code implementations • 29 May 2024 • Mingmeng Geng, Sihong He, Roberto Trotta
Do large language models (LLMs) have their own worldviews and personality tendencies?
no code implementations • 29 May 2024 • Han Wang, Sihong He, Zhili Zhang, Fei Miao, James Anderson
In contrast to existing results, we demonstrate that both FedSVRPG-M and FedHAPG-M, both of which leverage momentum mechanisms, can exactly converge to a stationary point of the average performance function, regardless of the magnitude of environment heterogeneity.
no code implementations • 2 May 2024 • Zhongchang Sun, Sihong He, Fei Miao, Shaofeng Zou
Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment.
no code implementations • 30 Jul 2023 • Sihong He, Shuo Han, Fei Miao
In this work, we design a multi-agent reinforcement learning (MARL)-based framework for EAVs balancing in E-AMoD systems, with adversarial agents to model both the EAVs supply and mobility demand uncertainties that may undermine the vehicle balancing solutions.
1 code implementation • 30 Jul 2023 • Sihong He, Songyang Han, Sanbao Su, Shuo Han, Shaofeng Zou, Fei Miao
Then we propose a robust multi-agent Q-learning (RMAQ) algorithm to find such an equilibrium, with convergence guarantees.
1 code implementation • 6 Dec 2022 • Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, Shaofeng Zou, Fei Miao
Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents' policies are based on accurate state information.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 17 Sep 2022 • Sihong He, Yue Wang, Shuo Han, Shaofeng Zou, Fei Miao
In this work, we design a robust and constrained multi-agent reinforcement learning (MARL) framework with state transition kernel uncertainty for EV AMoD systems.
1 code implementation • 16 Sep 2022 • Sanbao Su, Yiming Li, Sihong He, Songyang Han, Chen Feng, Caiwen Ding, Fei Miao
Our work is the first to estimate the uncertainty of collaborative object detection.