梳理深度学习时代模仿学习的新进展与分类体系
Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances
- 提出全新分类框架，更好反映当前模仿学习研究格局
- 系统总结方法创新，涵盖泛化、数据偏差等核心挑战
- 适合关注智能体行为学习与自动驾驶的科研人员
模仿学习（IL）使智能体通过观察和复制一个或多个专家的行为来获取技能。近年来，深度学习的进步显著拓展了模仿学习在多个领域的应用能力与可扩展性，专家数据形式从完整的状态-动作轨迹到部分观测或无标签序列不等。伴随这一发展，新方法不断涌现，旨在解决长期存在的泛化、协变量偏移和示范质量等挑战。本文综述模仿学习研究的最新进展，重点分析近期趋势、方法创新与实际应用。我们提出一种新颖的分类体系，区别于现有划分方式，更准确反映当前模仿学习的研究现状与演进方向。文中对代表性工作的优势、局限及评估方法进行批判性分析，并指出未来研究的关键挑战与开放问题。
原文摘要 · Abstract (English)
Imitation learning (IL) enables agents to acquire skills by observing and replicating the behavior of one or multiple experts. In recent years, advances in deep learning have significantly expanded the capabilities and scalability of imitation learning across a range of domains, where expert data can range from full state-action trajectories to partial observations or unlabeled sequences. Alongside this growth, novel approaches have emerged, with new methodologies being developed to address longstanding challenges such as generalization, covariate shift, and demonstration quality. In this survey, we review the latest advances in imitation learning research, highlighting recent trends, methodological innovations, and practical applications. We propose a novel taxonomy that is distinct from existing categorizations to better reflect the current state of the IL research stratum and its trends. Throughout the survey, we critically examine the strengths, limitations, and evaluation practices of representative works, and we outline key challenges and open directions for future research.
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