Reinforcement Learning for Fail-Operational Systems with Disentangled Dual-Skill Variables

We present a novel approach to reinforcement learning (RL) specifically designed for fail-operational systems in critical safety applications. Our technique incorporates disentangled skill variables, significantly enhancing the resilience of conventional RL frameworks against mechanical failures and...

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Bibliographic Details

Published in:	Technologies
Main Authors:	Taewoo Kim, Shiho Kim
Format:	Article
Language:	English
Published:	MDPI AG 2025-04-01
Subjects:	reinforcement learning skill-based learning operational safety fail-operational system
Online Access:	https://www.mdpi.com/2227-7080/13/4/156