Continual deep reinforcement learning with task-agnostic policy distillation

Continual deep reinforcement learning with task-agnostic policy distillation

Abstract Central to the development of universal learning systems is the ability to solve multiple tasks without retraining from scratch when new data arrives. This is crucial because each task requires significant training time. Addressing the problem of continual learning necessitates various meth...

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Bibliographic Details
Main Authors:	Muhammad Burhan Hafez, Kerim Erekmen
Format:	Article
Language:	English
Published:	Nature Portfolio 2024-12-01
Series:	Scientific Reports
Subjects:	Continual learning Reinforcement learning Self-supervised learning Task-agnostic learning
Online Access:	https://doi.org/10.1038/s41598-024-80774-8
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