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(2024) A Survey on Interpretable Reinforcement Learning
(2024) A Survey on Interpretable Reinforcement Learning
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Table of Contents
1. Interpretability vs Explainability
2. Taxonomy: RL ํ์ดํ๋ผ์ธ์ ๊ตฌ์ฑ์์๋ก ์ชผ๊ฐ ๋ค
2.1 Interpretable Inputs (์ ๋ ฅ์ ํด์ ๊ฐ๋ฅ์ฑ)
2.2 Interpretable Transition Models (์ ์ด/dynamics ๋ชจ๋ธ)
2.3 Interpretable Preference/Reward Models (์ ํธ/๋ณด์ ๋ชจ๋ธ)
2.4 Interpretable Policy (์ ์ฑ ์ ํด์ ๊ฐ๋ฅ์ฑ)
3. ํต์ฌ ๋์
4. ์ค ์ ์ฉยท๊ตฌํ ๊ด์ ์์์ ์๊ฒฌ (FAB MachineโLot ํ ๋น ์ค๋ช Agent)
์ฐธ๊ณ ๋ฌธํ
Backlinks
(2025) Design Patterns of Deep Reinforcement Learning Models for Job Shop Scheduling Problems