Learning Top-K Subtask Planning Tree Based on Discriminative Representation Pretraining for Decision-making | |
Jingqing Ruan2,3; Kaishen Wang1,3; Qingyang Zhang2,3; Dengpeng Xing1,3; Bo Xu1,3 | |
刊名 | Machine Intelligence Research
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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. |
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