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Imitation learning: a survey of learning methods.

Hussein, Ahmed; Gaber, Mohamed Medhat; Elyan, Eyad; Jayne, Chrisina


Ahmed Hussein

Mohamed Medhat Gaber

Chrisina Jayne


Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years, however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations; without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction and computer games to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this paper, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications and highlight current and future research directions.


HUSSEIN, A., GABER, M.M., ELYAN, E. and JAYNE, C. 2017. Imitation learning: a survey of learning methods. ACM computing surveys [online], 50(2), article 21. Available from:

Journal Article Type Article
Acceptance Date Jan 31, 2017
Online Publication Date Apr 11, 2017
Publication Date Jun 30, 2017
Deposit Date May 10, 2017
Publicly Available Date May 10, 2017
Journal ACM computing surveys
Print ISSN 0360-0300
Electronic ISSN 1557-7341
Publisher Association for Computing Machinery (ACM)
Peer Reviewed Peer Reviewed
Volume 50
Issue 2
Article Number 21
Keywords Learning paradigms; Learning settings; Machine learning approaches; Cognitive robotics; Control methods; Distributed artificial intelligence; Computer vision; Imitation learning
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