Embodied imitation-enhanced reinforcement learning in multi-agent systems

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

Abstract

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.

Citation

Erbas, M. D., Winfield, A. F., & Bull, L. (2014). Embodied imitation-enhanced reinforcement learning in multi-agent systems. Adaptive Behavior, 22(1), 31-50. https://doi.org/10.1177/1059712313500503

• 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
• DOI: https://doi.org/10.1177/1059712313500503
• Keywords: embodied imitation, reinforcement q-learning, social learning, multi-agent systems
• Public URL: https://uwe-repository.worktribe.com/output/821497
• Publisher URL: http://dx.doi.org/10.1177/1059712313500503