Learning Top-K Subtask Planning Tree Based on Discriminative Representation Pretraining for Decision-making

doi:10.1007/s11633-023-1483-z

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	Learning Top-K Subtask Planning Tree Based on Discriminative Representation Pretraining for Decision-making
	Jingqing Ruan 2,3; Kaishen Wang 1,3; Qingyang Zhang 2,3; Dengpeng Xing 1,3; Bo Xu 1,3
刊名	Machine Intelligence Research
	2024
卷号	21 期号:4 页码:782-800
关键词	Reinforcement learning representation learning subtask planning task decomposition pretraining.
ISSN号	2731-538X
DOI	10.1007/s11633-023-1483-z
英文摘要	Decomposing complex real-world tasks into simpler subtasks and devising a subtask execution plan is critical for humans to achieve effective decision-making. However, replicating this process remains challenging for AI agents and naturally raises two questions: 1) How to extract discriminative knowledge representation from priors? 2) How to develop a rational plan to decompose complex problems? To address these issues, we introduce a groundbreaking framework that incorporates two main contributions. First, our multiple-encoder and individual-predictor regime goes beyond traditional architectures to extract nuanced task-specific dynamics from datasets, enriching the feature space for subtasks. Second, we innovate in planning by introducing a top- subtask planning tree generated through an attention mechanism, which allows for dynamic adaptability and forward-looking decision-making. Our framework is empirically validated against challenging benchmarks BabyAI including multiple combinatorially rich synthetic tasks (e.g., GoToSeq, SynthSeq, BossLevel), where it not only outperforms competitive baselines but also demonstrates superior adaptability and effective ness in complex task decomposition.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/58572]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 2.School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China 3.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
推荐引用方式 GB/T 7714	Jingqing Ruan, Kaishen Wang, Qingyang Zhang,et al. Learning Top-K Subtask Planning Tree Based on Discriminative Representation Pretraining for Decision-making[J]. Machine Intelligence Research,2024,21(4):782-800.
APA	Jingqing Ruan, Kaishen Wang, Qingyang Zhang, Dengpeng Xing,& Bo Xu.(2024).Learning Top-K Subtask Planning Tree Based on Discriminative Representation Pretraining for Decision-making.Machine Intelligence Research,21(4),782-800.
MLA	Jingqing Ruan,et al."Learning Top-K Subtask Planning Tree Based on Discriminative Representation Pretraining for Decision-making".Machine Intelligence Research 21.4(2024):782-800.