論文筆記 Joint Inference of Reward Machines and Policies for Reiforcement Learning
摘要 吸取高階知識（high-level knowledge）是加快強化學習的一個有效途徑。論文研究了一種強化學習問題，其中高階知識是以reward machines的形式存在的。reward machine是Mealy狀態機（Mealy machine）的一類，使用了非馬爾科夫（non-Markovian，獎勵不僅依賴於當前狀態，也依賴於歷史狀態）的獎勵函數（reward function）。論
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相關文章
- 1. 【論文筆記】Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations
- 2. 【論文筆記】Joint Unsupervised Learning of Deep Representations and Image Clusters
- 3. 《Joint Learning of Named Entity Recognition and Entity Linking》論文筆記
- 4. 1604.Joint Detection and Identification Feature Learning for Person Search論文閱讀筆記
- 5. ICCV 2017 EAST:《Learning Policies for Adaptive Tracking with Deep Feature Cascades》論文筆記
- 6. 論文筆記 之 Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow
- 7. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 8. 論文筆記_2018-ECCV-Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
- 9. 論文筆記之--Joint Detection and Identification Feature Learning for Person Search
- 10. 1607.CVPR-Joint Learning of Single-image and Cross-image Representations for Person ReID 論文筆記