12 citations · 49 across the 33 of their papers we have counts for
14 papers · 1 filter
Differentiable Adaptive Kalman Filtering via Optimal Transport
Yangguang He, Wenhao Li, Minzhe Li +3
Learning-based filtering has demonstrated strong performance in non-linear dynamical systems, particularly when the statistics of noise are unknown. However, in real-world deployme…
Interpretable Hybrid-Rule Temporal Point Processes
Yunyang Cao, Juekai Lin, Hongye Wang +2
Temporal Point Processes (TPPs) are widely used for modeling event sequences in various medical domains, such as disease onset prediction, progression analysis, and clinical decisi…
Scalable Reinforcement Learning for Virtual Machine Scheduling
Junjie Sheng, Jiehao Wu, Haochuan Cui +6
Recent advancements in reinforcement learning (RL) have shown promise for optimizing virtual machine scheduling (VMS) in small-scale clusters. The utilization of RL to large-scale…
TrackDiffuser: Nearly Model-Free Bayesian Filtering with Diffusion Model
Yangguang He, Wenhao Li, Minzhe Li +3
State estimation remains a fundamental challenge across numerous domains, from autonomous driving, aircraft tracking to quantum system control. Although Bayesian filtering has been…
GraphThought: Graph Combinatorial Optimization with Thought Generation
Zixiao Huang, Lifeng Guo, Wenhao Li +6
Graph combinatorial optimization (GCO) problems are central to domains like logistics and bioinformatics. While traditional solvers dominate, large language models (LLMs) offer new…
Carbon Market Simulation with Adaptive Mechanism Design
Han Wang, Wenhao Li, Hongyuan Zha +1
A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility, i.e., reducing carbon emissions to tackle climate chan…
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning
Wenhao Li, Dan Qiao, Baoxiang Wang +3
The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved. Autom…
Diverse Policy Optimization for Structured Action Space
Wenhao Li, Baoxiang Wang, Shanchao Yang +1
Enhancing the diversity of policies is beneficial for robustness, exploration, and transfer in reinforcement learning (RL). In this paper, we aim to seek diverse policies in an und…
VMAgent: Scheduling Simulator for Reinforcement Learning
Junjie Sheng, Shengliang Cai, Haochuan Cui +9
A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling. VMAgent is inspired by practical virtua…
Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning
Wenhao Li, Xiangfeng Wang, Bo Jin +3
When solving a complex task, humans will spontaneously form teams and to complete different parts of the whole task, respectively. Meanwhile, the cooperation between teammates will…
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition
Wenhao Li, Xiangfeng Wang, Bo Jin +2
Non-stationarity is one thorny issue in cooperative multi-agent reinforcement learning (MARL). One of the reasons is the policy changes of agents during the learning process. Some…
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning
Wenhao Li, Bo Jin, Xiangfeng Wang +2
Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications, due to non-interactivity between agents, curse o…
Learning Structured Communication for Multi-agent Reinforcement Learning
Junjie Sheng, Xiangfeng Wang, Bo Jin +5
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting. We summarize the general categories of topology fo…
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem
Yun Hua, Xiangfeng Wang, Bo Jin +4
In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward. In thi…