53 citations · 92 across the 10 of their papers we have counts for
10 papers
Towards VM Rescheduling Optimization Through Deep Reinforcement Learning
Xianzhong Ding, Yunkai Zhang, Binbin Chen +6
Modern industry-scale data centers need to manage a large number of virtual machines (VMs). Due to the continual creation and release of VMs, many small resource fragments are scat…
Golden-Retriever: High-Fidelity Agentic Retrieval Augmented Generation for Industrial Knowledge Base
Zhiyu An, Xianzhong Ding, Yen-Chun Fu +3
This paper introduces Golden-Retriever, designed to efficiently navigate vast industrial knowledge bases, overcoming challenges in traditional LLM fine-tuning and RAG frameworks wi…
A Safe and Data-efficient Model-based Reinforcement Learning System for HVAC Control
Xianzhong Ding, Zhiyu An, Arya Rathee +1
Model-Based Reinforcement Learning (MBRL) has been widely studied for Heating, Ventilation, and Air Conditioning (HVAC) control in buildings. One of the critical challenges is the…
Go Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control
Zhiyu An, Xianzhong Ding, Wan Du
Recent research has shown the potential of Model-based Reinforcement Learning (MBRL) to enhance energy efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems. Howe…
Reward Bound for Behavioral Guarantee of Model-based Planning Agents
Zhiyu An, Xianzhong Ding, Wan Du
Recent years have seen an emerging interest in the trustworthiness of machine learning-based agents in the wild, especially in robotics, to provide safety assurance for the industr…
HPC-GPT: Integrating Large Language Model for High-Performance Computing
Xianzhong Ding, Le Chen, Murali Emani +6
Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks. However, their perform…
Data Race Detection Using Large Language Models
Le Chen, Xianzhong Ding, Murali Emani +3
Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumv…
Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Field
Xianzhong Ding, Wan Du
Agricultural irrigation is a significant contributor to freshwater consumption. However, the current irrigation systems used in the field are not efficient. They rely mainly on soi…
Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning
Xianzhong Ding, Alberto Cerpa, Wan Du
In this paper, we conduct a set of experiments to analyze the limitations of current MBRL-based HVAC control methods, in terms of model uncertainty and controller effectiveness. Us…
Exploring Deep Reinforcement Learning for Holistic Smart Building Control
Xianzhong Ding, Alberto Cerpa, Wan Du
In this paper, we take a holistic approach to deal with the tradeoffs between energy use and comfort in commercial buildings. We developed a system called OCTOPUS, which employs a…