1 citations · 2 across the 5 of their papers we have counts for
10 papers
ERFSL: An Efficient Reward Function Searcher via Language Models for Custom-Environment Multi-Objective Optimization (Student Abstract)
Guanwen Xie, Jingzehua Xu, Yiyuan Yang +2
We propose ERFSL, an efficient reward function searcher using large language models (LLMs) for custom-environment, multi-objective learning-based methods (LB). ERFSL generates rewa…
AoI-MDP: An AoI Optimized Markov Decision Process (Student Abstract)
Yimian Ding, Jingzehua Xu, Yiyuan Yang +3
Ocean exploration places high demands on autonomous underwater vehicles, especially when there's observation delay. We propose age of information optimized Markov decision process…
Multi-Objective-Optimization Assisted Data Collection Framework for IoUT Based on Offline Reinforcement
Yimian Ding, Xinqi Wang, Jingzehua Xu +3
The Information Updating Networks (IUNs) offers significant potential for ocean exploration but encounters challenges due to dynamic underwater environments and severe system atten…
Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement
Guanwen Xie, Jingzehua Xu, Yiyuan Yang +2
Achieving the effective design and improvement of reward functions in reinforcement learning (RL) tasks with complex custom environments and multiple requirements presents consider…
Enhancing Information Freshness: An AoI Optimized Markov Decision Process
Jingzehua Xu, Yimian Ding, Yiyuan Yang +2
Ocean exploration utilizing autonomous underwater vehicles (AUVs) via reinforcement learning (RL) has emerged as a significant research focus. However, underwater tasks have mostly…
Never too Cocky to Cooperate: An FIM and RL-based USV-AUV Collaborative System for Underwater Tasks in Extreme Sea Conditions
Jingzehua Xu, Guanwen Xie, Jiwei Tang +5
This paper develops a novel unmanned surface vehicle (USV)-autonomous underwater vehicle (AUV) collaborative system designed to enhance underwater task performance in extreme sea c…