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(2023) Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
(2023) Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
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ai-scheduling
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xai
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Table of Contents
1. 출발점: DRAS, DQN 기반 클러스터 스케줄러
2. IRL 프레임워크: imitation learning으로 트리 추출
2.1 분포 shift 문제와 DAgger
2.2 트리가 너무 커지는 문제와 critical state pruning
3. 트리를 읽으면 보상 설계가 보인다
4. 실험 결과
5. 실 적용·구현 관점에서의 의견 (FAB Machine–Lot 할당 설명 Agent)
참고문헌
Backlinks
(2024) Explainable Reinforcement Learning (XRL): A Systematic Literature Review and Taxonomy
(2024) A Survey on Interpretable Reinforcement Learning
(2026) Explainable AI for Reinforcement Learning Based Dynamic Scheduling Solutions in Semiconductor Manufacturing
(2025) Design Patterns of Deep Reinforcement Learning Models for Job Shop Scheduling Problems