Zobrazeno 1 - 10
of 7 843
pro vyhledávání: '"SONG Lei"'
Multi-objective reinforcement learning (MORL) excels at handling rapidly changing preferences in tasks that involve multiple criteria, even for unseen preferences. However, previous dominating MORL methods typically generate a fixed policy set or pre
Externí odkaz:
http://arxiv.org/abs/2410.02236
Recently, the pre-training of decision transformers (DT) using a different domain, such as natural language text, has generated significant attention in offline reinforcement learning (Offline RL). Although this cross-domain pre-training approach ach
Externí odkaz:
http://arxiv.org/abs/2409.06985
Autor:
Khallaghi, Sam, Abedi, Rahebe, Ali, Hanan Abou, Alemohammad, Hamed, Asipunu, Mary Dziedzorm, Alatise, Ismail, Ha, Nguyen, Luo, Boka, Mai, Cat, Song, Lei, Wussah, Amos, Xiong, Sitian, Yao, Yao-Ting, Zhang, Qi, Estes, Lyndon D.
The accuracy of mapping agricultural fields across large areas is steadily improving with high-resolution satellite imagery and deep learning (DL) models, even in regions where fields are small and geometrically irregular. However, developing effecti
Externí odkaz:
http://arxiv.org/abs/2408.06467
Autor:
Choi, Yunseon, Bae, Sangmin, Ban, Seonghyun, Jeong, Minchan, Zhang, Chuheng, Song, Lei, Zhao, Li, Bian, Jiang, Kim, Kee-Eung
With the advent of foundation models, prompt tuning has positioned itself as an important technique for directing model behaviors and eliciting desired responses. Prompt tuning regards selecting appropriate keywords included into the input, thereby a
Externí odkaz:
http://arxiv.org/abs/2407.14733
Retrieval augmented generation has revolutionized large language model (LLM) outputs by providing factual supports. Nevertheless, it struggles to capture all the necessary knowledge for complex reasoning questions. Existing retrieval methods typicall
Externí odkaz:
http://arxiv.org/abs/2406.06572
Recent advancements in solving large-scale traveling salesman problems (TSP) utilize the heatmap-guided Monte Carlo tree search (MCTS) paradigm, where machine learning (ML) models generate heatmaps, indicating the probability distribution of each edg
Externí odkaz:
http://arxiv.org/abs/2406.03503
Autor:
Liu, Zhihao, Yang, Xianliang, Liu, Zichuan, Xia, Yifan, Jiang, Wei, Zhang, Yuanyu, Li, Lijuan, Fan, Guoliang, Song, Lei, Jiang, Bian
Multi-agent reinforcement learning (MARL) is employed to develop autonomous agents that can learn to adopt cooperative or competitive strategies within complex environments. However, the linear increase in the number of agents leads to a combinatoria
Externí odkaz:
http://arxiv.org/abs/2405.16854
Autor:
Liu, Zichuan, Wang, Tianchun, Shi, Jimeng, Zheng, Xu, Chen, Zhuomin, Song, Lei, Dong, Wenqian, Obeysekera, Jayantha, Shirani, Farhad, Luo, Dongsheng
Explaining deep learning models operating on time series data is crucial in various applications of interest which require interpretable and transparent insights from time series signals. In this work, we investigate this problem from an information
Externí odkaz:
http://arxiv.org/abs/2405.09308
Autor:
Liu, Zichuan, Wang, Zefan, Xu, Linjie, Wang, Jinyu, Song, Lei, Wang, Tianchun, Chen, Chunlin, Cheng, Wei, Bian, Jiang
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content. Despite efforts to ethically align LLMs, these are often fragile and can be circumvented by
Externí odkaz:
http://arxiv.org/abs/2404.13968
Autor:
Xu, Linjie, Liu, Zichuan, Dockhorn, Alexander, Perez-Liebana, Diego, Wang, Jinyu, Song, Lei, Bian, Jiang
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency. Compared to single agent RL, the sample efficiency for Multi-Agent Reinforcement Learning (MARL) is more challenging because of its inherent partial observability,
Externí odkaz:
http://arxiv.org/abs/2404.09715