An Invitation to Imitation
An Invitation to Imitation
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DOI：
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发表时间：
2015-03
影响因子：
12
通讯作者：
Andrew Bagnell;March
中科院分区：
文献类型：
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作者：
Andrew Bagnell;March
翻译后摘要：模仿学习的算法，试图通过模仿老师的决定和行为，以提高性能的研究。这些技术有望通过演示来实现有效的编程，以自动化人们可以演示但难以手动编程的任务，例如驾驶。这项工作是一个总结，从一个非常个人的角度研究计算有效的方法学习模仿行为。我打算为两个受众服务：让机器学习专家参与模仿学习的挑战，以及与更熟悉的框架（如统计监督学习理论）之间有趣的理论和实践区别;同样，让机器人专家和应用人工智能专家更广泛地欣赏模仿学习的框架和工具。
Abstract : Imitation learning is the study of algorithms that attempt to improve performance by mimicking a teacher's decisions and behaviors. Such techniques promise to enable effective programming by demonstration to automate tasks, such as driving, that people can demonstrate but find difficult to hand program. This work represents a summary from a very personal perspective of research on computationally effective methods for learning to imitate behavior. I intend it to serve two audiences: to engage machine learning experts in the challenges of imitation learning and the interesting theoretical and practical distinctions with more familiar frameworks like statistical supervised learning theory; and equally, to make the frameworks and tools available for imitation learning more broadly appreciated by roboticists and experts in applied artificial intelligence.