Adaptive co-evolution using reinforcement learning

The field of co-evolution is focused on evolving agents based on the fitness of another evolving agent; in this way, agents are expected to learn from each other. This research aims to develop agents, which would adapt to the skill level of an individual human. Particularly, this study investigates...

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Main Authors:	Bulos, Remedios de Dios, Amurao, Rex Benedict, Gallardo, Allan David, Moreno, Marvin Royce
Format:	text
Published:	Animo Repository 2007
Subjects:	Other Engineering Robotics
Online Access:	https://animorepository.dlsu.edu.ph/faculty_research/3229
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Institution:	De La Salle University

id	oai:animorepository.dlsu.edu.ph:faculty_research-4172
record_format	eprints
spelling	oai:animorepository.dlsu.edu.ph:faculty_research-41722022-07-22T06:41:25Z Adaptive co-evolution using reinforcement learning Bulos, Remedios de Dios Amurao, Rex Benedict Gallardo, Allan David Moreno, Marvin Royce The field of co-evolution is focused on evolving agents based on the fitness of another evolving agent; in this way, agents are expected to learn from each other. This research aims to develop agents, which would adapt to the skill level of an individual human. Particularly, this study investigates and discusses the behavior of reinforcement learning (RL) as an algorithm for co-evolution. A modified snake game was used to test and evaluate co-evolutionary behavior exhibited by the RL algorithm against genetic algorithm and best response learning algorithm 2007-03-15T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/3229 Faculty Research Work Animo Repository Other Engineering Robotics
institution	De La Salle University
building	De La Salle University Library
continent	Asia
country	Philippines Philippines
content_provider	De La Salle University Library
collection	DLSU Institutional Repository
topic	Other Engineering Robotics
spellingShingle	Other Engineering Robotics Bulos, Remedios de Dios Amurao, Rex Benedict Gallardo, Allan David Moreno, Marvin Royce Adaptive co-evolution using reinforcement learning
description	The field of co-evolution is focused on evolving agents based on the fitness of another evolving agent; in this way, agents are expected to learn from each other. This research aims to develop agents, which would adapt to the skill level of an individual human. Particularly, this study investigates and discusses the behavior of reinforcement learning (RL) as an algorithm for co-evolution. A modified snake game was used to test and evaluate co-evolutionary behavior exhibited by the RL algorithm against genetic algorithm and best response learning algorithm
format	text
author	Bulos, Remedios de Dios Amurao, Rex Benedict Gallardo, Allan David Moreno, Marvin Royce
author_facet	Bulos, Remedios de Dios Amurao, Rex Benedict Gallardo, Allan David Moreno, Marvin Royce
author_sort	Bulos, Remedios de Dios
title	Adaptive co-evolution using reinforcement learning
title_short	Adaptive co-evolution using reinforcement learning
title_full	Adaptive co-evolution using reinforcement learning
title_fullStr	Adaptive co-evolution using reinforcement learning
title_full_unstemmed	Adaptive co-evolution using reinforcement learning
title_sort	adaptive co-evolution using reinforcement learning
publisher	Animo Repository
publishDate	2007
url	https://animorepository.dlsu.edu.ph/faculty_research/3229
_version_	1740844662237691904

Adaptive co-evolution using reinforcement learning

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