Accelerating evolution via egalitarian social learning

Abstract

Social learning is an extension to evolutionary algorithms that enables agents to learn from observations of others in the population. Historically, social learning algorithms have employed a student-teacher model where the behavior of one or more high-fitness agents is used to train a subset of the remaining agents in the population. This paper presents ESL, an egalitarian model of social learning in which agents are not labeled as teachers or students, instead allowing any individual receiving a sufficiently high reward to teach other agents to mimic its recent behavior. We validate our approach through a series of experiments in a robot foraging domain, including comparisons of egalitarian social learning with baseline neuroevolution and a variant of studentteacher social learning. In a complex foraging task, ESL converges to near-optimal strategies faster than either benchmark approach, outperforming both by more than an order of magnitude. The results indicate that egalitarian social learning is a promising new paradigm for social learning in intelligent agents.

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

“…There is ample evidence that set-ups, where robots can share knowledge, outperform otherwise equivalent set-ups where robots learn in isolation.When robots share knowledge, they achieve better performance and/or the learning curve is steeper (Usui and Arita, 2003 ; Curran and ORiordan, 2007 ; Perez et al, 2008 ; Pugh and Martinoli, 2009 ; Garca-Sanchez et al, 2012 ; Miikkulainen et al, 2012 ; Tansey et al, 2012 ; Heinerman et al, 2015a , b ; Jolley et al, 2016 ). A higher overall performance can be observed when there is a quality or diversity assessment before the knowledge is sent or incorporated (Huijsman et al, 2011 ; Garca-Sanchez et al, 2012 ; Heinerman et al, 2015b ).…”

Evolution, Individual Learning, and Social Learning in a Swarm of Real Robots

“…Our work presented here is different because we have a three-tier adaptive system with evolution in the genome space and individual and social learning in the memome space. This is not a new concept in simulation [8], [15], [16], [17], [18] but to our best knowledge it has not been implemented in real hardware before.…”

Is social learning more than parameter tuning?

“…e literature o ers examples where robots trained with social learning reach a higher performance compared to their individual learning counterparts [e.g, 2,4]. No explanation has been advanced for that observation.…”