Original Research ARTICLE
Importance of Parameter Settings on the Benefits of Robot-to-Robot Learning
- 1VU University Amsterdam, Netherlands
Robot-to-robot learning, a specific case of social learning in robotics, enables multiple robots to share learned skills while completing a task. The literature offers various statements of its benefits. Robots using this type of social learning can reach a higher performance, an increased learning speed, or both, compared to robots using individual learning only. No general explanation has been advanced for the difference in observations, which make the results highly dependent on the particular system and parameter setting.
In this paper, we perform a detailed analysis into the effects of robot-to-robot learning. As a result, we show that this type of social learning can reduce the sensitivity of the learning process to the choice of parameters in two ways. First, robot-to-robot learning can reduce the number of bad performing individuals in the population. Second, robot-to-robot learning can increase the chance of having a successful run, where success is defined as the presence of a high performing individual.
Additionally, we show that robot-to-robot learning results in an increased learning speed for almost all parameter settings.
Our results indicate that robot-to-robot learning is a powerful mechanism which leads to benefits in both performance and learning speed.
Keywords: Social learning, Robot-to-robot learning, Evolutionary Robotics, parameter tuning, evolutionary algorithm
Received: 02 Aug 2018;
Accepted: 04 Feb 2019.
Edited by:Mel Slater, University of Barcelona, Spain
Reviewed by:Eiji Uchibe, Advanced Telecommunications Research Institute International (ATR), Japan
Andrej Gams, Jožef Stefan Institute (IJS), Slovenia
Copyright: © 2019 Heinerman, Haasdijk and Eiben. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mrs. Jacqueline Heinerman, VU University Amsterdam, Amsterdam, Netherlands, firstname.lastname@example.org