Search Results for author:
Found 7 papers, 1 papers with code
Go Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control
We found that the high dimensionality of the thermal dynamics model input hinders the efficiency of policy extraction.
Reward Bound for Behavioral Guarantee of Model-based Planning Agents
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 industry.
HPC-GPT: Integrating Large Language Model for High-Performance Computing
Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks.
Data Race Detection Using Large Language Models
Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumventing the need for resource-intensive manual tool creation.
Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Field
The system employs a neural network, known as the DRL control agent, which learns an optimal control policy that considers both the current soil moisture measurement and the future soil moisture loss.
Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning
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.
Exploring Deep Reinforcement Learning for Holistic Smart Building Control
The DRL architecture includes a novel reward function that allows the framework to explore the tradeoffs between energy use and users' comfort, while at the same time enabling the solution of the high-dimensional control problem due to the interactions of four different building subsystems.
