Automated curriculum learning for embodied agents a neuroevolutionary approach

Abstract We demonstrate how the evolutionary training of embodied agents can be extended with a curriculum learning algorithm that automatically selects the environmental conditions in which the evolving agents are evaluated. The environmental conditions are selected to adjust the level of difficult...

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Autores principales:	Nicola Milano, Stefano Nolfi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/526589e900f34ccc951da87100eb776a
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spelling	oai:doaj.org-article:526589e900f34ccc951da87100eb776a2021-12-02T17:15:17ZAutomated curriculum learning for embodied agents a neuroevolutionary approach10.1038/s41598-021-88464-52045-2322https://doaj.org/article/526589e900f34ccc951da87100eb776a2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88464-5https://doaj.org/toc/2045-2322Abstract We demonstrate how the evolutionary training of embodied agents can be extended with a curriculum learning algorithm that automatically selects the environmental conditions in which the evolving agents are evaluated. The environmental conditions are selected to adjust the level of difficulty to the ability level of the current evolving agents, and to challenge the weaknesses of the evolving agents. The method does not require domain knowledge and does not introduce additional hyperparameters. The results collected on two benchmark problems, that require to solve a task in significantly varying environmental conditions, demonstrate that the method proposed outperforms conventional learning methods and generates solutions which are robust to variations and able to cope with different environmental conditions.Nicola MilanoStefano NolfiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nicola Milano Stefano Nolfi Automated curriculum learning for embodied agents a neuroevolutionary approach
description	Abstract We demonstrate how the evolutionary training of embodied agents can be extended with a curriculum learning algorithm that automatically selects the environmental conditions in which the evolving agents are evaluated. The environmental conditions are selected to adjust the level of difficulty to the ability level of the current evolving agents, and to challenge the weaknesses of the evolving agents. The method does not require domain knowledge and does not introduce additional hyperparameters. The results collected on two benchmark problems, that require to solve a task in significantly varying environmental conditions, demonstrate that the method proposed outperforms conventional learning methods and generates solutions which are robust to variations and able to cope with different environmental conditions.
format	article
author	Nicola Milano Stefano Nolfi
author_facet	Nicola Milano Stefano Nolfi
author_sort	Nicola Milano
title	Automated curriculum learning for embodied agents a neuroevolutionary approach
title_short	Automated curriculum learning for embodied agents a neuroevolutionary approach
title_full	Automated curriculum learning for embodied agents a neuroevolutionary approach
title_fullStr	Automated curriculum learning for embodied agents a neuroevolutionary approach
title_full_unstemmed	Automated curriculum learning for embodied agents a neuroevolutionary approach
title_sort	automated curriculum learning for embodied agents a neuroevolutionary approach
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/526589e900f34ccc951da87100eb776a
work_keys_str_mv	AT nicolamilano automatedcurriculumlearningforembodiedagentsaneuroevolutionaryapproach AT stefanonolfi automatedcurriculumlearningforembodiedagentsaneuroevolutionaryapproach
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Automated curriculum learning for embodied agents a neuroevolutionary approach

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