AUTHOR=Kaiser Jacques , Hoff Michael , Konle Andreas , Vasquez Tieck J. Camilo , Kappel David , Reichard Daniel , Subramoney Anand , Legenstein Robert , Roennau Arne , Maass Wolfgang , Dillmann Rüdiger TITLE=Embodied Synaptic Plasticity With Online Reinforcement Learning JOURNAL=Frontiers in Neurorobotics VOLUME=13 YEAR=2019 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2019.00081 DOI=10.3389/fnbot.2019.00081 ISSN=1662-5218 ABSTRACT=

The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in isolation on pattern classification tasks. This contrasts with the biological brain which purpose is to control a body in closed-loop. This paper contributes to bringing the fields of computational neuroscience and robotics closer together by integrating open-source software components from these two fields. The resulting framework allows to evaluate the validity of biologically-plausibe plasticity models in closed-loop robotics environments. We demonstrate this framework to evaluate Synaptic Plasticity with Online REinforcement learning (SPORE), a reward-learning rule based on synaptic sampling, on two visuomotor tasks: reaching and lane following. We show that SPORE is capable of learning to perform policies within the course of simulated hours for both tasks. Provisional parameter explorations indicate that the learning rate and the temperature driving the stochastic processes that govern synaptic learning dynamics need to be regulated for performance improvements to be retained. We conclude by discussing the recent deep reinforcement learning techniques which would be beneficial to increase the functionality of SPORE on visuomotor tasks.