Importance of Parameter Settings on the Benefits of Robot-to-Robot Learning in Evolutionary Robotics

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

Can Social Learning Increase Learning Speed, Performance or Both?

Social learning 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 research, we show that even within one system, the observed advantages of social learning can vary between parameter settings. Using Evolutionary Robotics, we train robots individually in a foraging task. We compare the performance of 50 parameter instances of the evolutionary algorithm obtained by a definitive screening design. We apply social learning in groups of two and four robots to the parameter settings that lead to the best and median performance. Our results show that the observed advantages of social learning differ highly between parameter settings but in general, median quality parameter settings experience more benefit from social learning. 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, these results suggest that social learning reduces the sensitivity of the learning process to the choice of parameters