AUTHOR=Nitschke Geoff , Didi Sabre 

TITLE=Evolutionary Policy Transfer and Search Methods for Boosting Behavior Quality: RoboCup Keep-Away Case Study

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 4 - 2017

YEAR=2017

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2017.00062

DOI=10.3389/frobt.2017.00062

ISSN=2296-9144

ABSTRACT=This study evaluates various evolutionary search methods to direct neural controller
evolution in company with policy (behavior) transfer across increasingly complex collective
robotic (RoboCup keep-away) tasks. That is, where robot behaviors are first
evolved in a source task and then transferred for further evolution to more complex
target tasks. Evolutionary search methods tested include objective-based search (fitness
function), behavioral and genotypic diversity maintenance and hybrids of such
diversity maintenance and objective-based search. Evolved behavior quality is evaluated
according to effectiveness and efficiency. Effectiveness is the average task
performance of transferred and evolved behaviors, where task performance is the average
time the ball is controlled by a keeper team. Efficiency is the average number
of generations taken for the fittest evolved behaviors to reach a minimum task performance
threshold given policy transfer. Results indicate that policy transfer coupled
with evolutionary search directed by hybridized behavioral diversity maintenance and
objective-based search addresses the bootstrapping problem for increasingly complex
keep-away tasks, in that this method evolves collective behaviors that could not be
evolved by comparative evolutionary methods (with and without policy transfer).