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Hindsight Experience Replay with Evolutionary Decision Trees for Curriculum Goal Generation Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Leveraging More of Biology in Evolutionary Reinforcement Learning

verfasst von : Bruno Gašperov, Marko Đurasević, Domagoj Jakobovic

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer Nature Switzerland

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Abstract

In this paper, we survey the use of additional biologically inspired mechanisms, principles, and concepts in the area of evolutionary reinforcement learning (ERL). While recent years have witnessed the emergence of a swath of metaphor-laden approaches, many merely echo old algorithms through novel metaphors. Simultaneously, numerous promising ideas from evolutionary biology and related areas, ripe for exploitation within evolutionary machine learning, remain in relative obscurity. To address this gap, we provide a comprehensive analysis of innovative, often unorthodox approaches in ERL that leverage additional bio-inspired elements. Furthermore, we pinpoint research directions in the field with the largest potential to yield impactful outcomes and discuss classes of problems that could benefit the most from such research.

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Vorheriges Kapitel Towards Physical Plausibility in Neuroevolution Systems
Nächstes Kapitel A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Search Behaviour
Fußnoten
1
It should be noted that ERL also includes approaches in which different state-action pairs are directly explored, as well as meta-RL methods.
 
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Metadaten
Titel
Leveraging More of Biology in Evolutionary Reinforcement Learning
verfasst von
Bruno Gašperov
Marko Đurasević
Domagoj Jakobovic
Copyright-Jahr
2024
Buch
Applications of Evolutionary Computation

Print ISBN: 978-3-031-56854-1

Electronic ISBN: 978-3-031-56855-8

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-3-031-56855-8_6

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