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자율주행 엣지의 CaSSLe 기반 오프라인 강화학습 모델 설계
A Design of a C&S-Lea-Based Offline Continual Reinforcement Learning Model in Autonomous Driving Edge Devices
- 이지은;
- 홍서희;
- 김지수;
- 김윤희
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0초록
There is growing interest in applying Reinforcement Learning (RL) to autonomous driving edge devices, which require both safety and real-time performance. We introduce a continual learning algorithm, CaSSLe (Continual Self-Supervised Learning), integrated with Double DQN-based reinforcement learning to enhance adaptability to new situations. Our proposed algorithm, AP-CaSSLe (Adaptive Progressive-Continual Self-Supervised Learning), dynamically adjusts the strength of input data augmentation. The AP-CaSSLe algorithm demonstrated a 58.9% improvement in accuracy compared to the Double DQN without continual learning, highlighting its potential for enhancing performance in autonomous driving edge environments.
키워드
continual learning; autonomous driving; reinforcement learning; edge; data augmentation; 연속학습; 자율주행; 강화학습; 엣지; 데이터 증강
- 제목
- 자율주행 엣지의 CaSSLe 기반 오프라인 강화학습 모델 설계
- 제목 (타언어)
- A Design of a C&S-Lea-Based Offline Continual Reinforcement Learning Model in Autonomous Driving Edge Devices
- 저자
- 이지은; 홍서희; 김지수; 김윤희
- 발행일
- 2026-04
- 유형
- Y
- 권
- 32
- 호
- 4
- 페이지
- 156 ~ 162