论文笔记 Joint Inference of Reward Machines and Policies for Reiforcement Learning

时间 2021-01-02

标签论文笔记强化学习繁體版

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摘要吸取高阶知识（high-level knowledge）是加快强化学习的一个有效途径。论文研究了一种强化学习问题，其中高阶知识是以reward machines的形式存在的。reward machine是Mealy状态机（Mealy machine）的一类，使用了非马尔科夫（non-Markovian，奖励不仅依赖于当前状态，也依赖于历史状态）的奖励函数（reward function）。论

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1. 【论文笔记】Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations
2. 【论文笔记】Joint Unsupervised Learning of Deep Representations and Image Clusters
3. 《Joint Learning of Named Entity Recognition and Entity Linking》论文笔记
4. 1604.Joint Detection and Identification Feature Learning for Person Search论文阅读笔记
5. ICCV 2017 EAST:《Learning Policies for Adaptive Tracking with Deep Feature Cascades》论文笔记
6. 论文笔记之 Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow
7. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 论文笔记
8. 论文笔记_2018-ECCV-Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
9. 论文笔记之--Joint Detection and Identification Feature Learning for Person Search
10. 1607.CVPR-Joint Learning of Single-image and Cross-image Representations for Person ReID 论文笔记

>>更多相关文章<<