Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Ruan, Jingqing"'
Autor:
Jiang, Haoyuan, Xiong, Xuantang, Li, Ziyue, Mao, Hangyu, Sui, Guanghu, Ruan, Jingqing, Cheng, Yuheng, Wei, Hua, Ketter, Wolfgang, Zhao, Rui
Currently, traffic signal control (TSC) methods based on reinforcement learning (RL) have proven superior to traditional methods. However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the c
Externí odkaz:
http://arxiv.org/abs/2407.10811
CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control
Autor:
Ruan, Jingqing, Li, Ziyue, Wei, Hua, Jiang, Haoyuan, Lu, Jiaming, Xiong, Xuantang, Mao, Hangyu, Zhao, Rui
Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite an amount of congestion, ev
Externí odkaz:
http://arxiv.org/abs/2405.17152
Causal dynamics models (CDMs) have demonstrated significant potential in addressing various challenges in reinforcement learning. To learn CDMs, recent studies have performed causal discovery to capture the causal dependencies among environmental var
Externí odkaz:
http://arxiv.org/abs/2405.12615
Autor:
Jiang, Li, Wu, Yusen, Xiong, Junwu, Ruan, Jingqing, Ding, Yichuan, Guo, Qingpei, Wen, Zujie, Zhou, Jun, Deng, Xiaotie
Publikováno v:
COLM 2024
Preference datasets are essential for incorporating human preferences into pre-trained language models, playing a key role in the success of Reinforcement Learning from Human Feedback. However, these datasets often demonstrate conflicting alignment o
Externí odkaz:
http://arxiv.org/abs/2405.11647
Autor:
Jiang, Haoyuan, Li, Ziyue, Wei, Hua, Xiong, Xuantang, Ruan, Jingqing, Lu, Jiaming, Mao, Hangyu, Zhao, Rui
The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights. However, a persisting issue remains: how to obtain a multi-agent traff
Externí odkaz:
http://arxiv.org/abs/2404.12090
Reinforcement learning has been revolutionizing the traditional traffic signal control task, showing promising power to relieve congestion and improve efficiency. However, the existing methods lack effective learning mechanisms capable of absorbing d
Externí odkaz:
http://arxiv.org/abs/2312.14532
Many complicated real-world tasks can be broken down into smaller, more manageable parts, and planning with prior knowledge extracted from these simplified pieces is crucial for humans to make accurate decisions. However, replicating this process rem
Externí odkaz:
http://arxiv.org/abs/2312.11027
Autor:
Zhang, Bin, Mao, Hangyu, Ruan, Jingqing, Wen, Ying, Li, Yang, Zhang, Shao, Xu, Zhiwei, Li, Dapeng, Li, Ziyue, Zhao, Rui, Li, Lijuan, Fan, Guoliang
The remarkable progress in Large Language Models (LLMs) opens up new avenues for addressing planning and decision-making problems in Multi-Agent Systems (MAS). However, as the number of agents increases, the issues of hallucination in LLMs and coordi
Externí odkaz:
http://arxiv.org/abs/2311.13884
Autor:
Kong, Yilun, Ruan, Jingqing, Chen, Yihong, Zhang, Bin, Bao, Tianpeng, Shi, Shiwei, Du, Guoqing, Hu, Xiaoru, Mao, Hangyu, Li, Ziyue, Zeng, Xingyu, Zhao, Rui
Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs.
Externí odkaz:
http://arxiv.org/abs/2311.11315
The previous state-of-the-art (SOTA) method achieved a remarkable execution accuracy on the Spider dataset, which is one of the largest and most diverse datasets in the Text-to-SQL domain. However, during our reproduction of the business dataset, we
Externí odkaz:
http://arxiv.org/abs/2310.18752