| Abstract |
Social learning in Evolutionary Robotics enables multiple robots to share learned experiences while completing a task. The literature offers contradicting examples of its benefits; robots trained with social learning reach a higher performance, an increased learning speed, or both, compared to their individual learning counterparts. 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 talk, I show that even within one system, the observed advantages of social learning can vary between parameter settings. These results serve as a reminder that tuning of the parameters should not be left as an afterthought because they can drastically impact the conclusions on the advantages of social learning. Additionally, the results show that lower quality parameter settings benefit more from social learning. This suggests that social learning reduces the sensitivity of the learning process to the choice of parameters. |