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Embodied imitation-enhanced reinforcement learning in multi-agent systems

Erbas, Mehmet D.; Winfield, Alan F.T.; Bull, Larry

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Mehmet D. Erbas

Lawrence Bull
School Director (Research & Enterprise) and Professor


Imitation is an example of social learning in which an individual observes and copies another's actions. This paper presents a new method for using imitation as a way of enhancing the learning speed of individual agents that employ a well-known reinforcement learning algorithm, namely Q-learning. Compared with other research that uses imitation with reinforcement learning, our method uses imitation of purely observed behaviours to enhance learning, with no internal state access or sharing of experiences between agents. The paper evaluates our imitation-enhanced reinforcement learning approach in both simulation and with real robots in continuous space. Both simulation and real robot experimental results show that the learning speed of the group is improved. © The Author(s) 2013.


Erbas, M. D., Winfield, A. F., & Bull, L. (2014). Embodied imitation-enhanced reinforcement learning in multi-agent systems. Adaptive Behavior, 22(1), 31-50.

Journal Article Type Article
Online Publication Date Aug 29, 2013
Publication Date Feb 1, 2014
Publicly Available Date Jun 6, 2019
Journal Adaptive Behavior
Print ISSN 1059-7123
Electronic ISSN 1741-2633
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 22
Issue 1
Pages 31-50
Keywords embodied imitation, reinforcement q-learning, social learning, multi-agent systems
Public URL
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