Reinforcement learning:toward action-knowledge merged intelligent mechanisms and algorithms

Fei-Yue WANG; Dongpu CAO; Qinglai WEI

doi:10.11959/j.issn.2096-6652.202011

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Reinforcement learning:toward action-knowledge merged intelligent mechanisms and algorithms

Review Intelligence | 更新时间：2024-06-05

- Reinforcement learning:toward action-knowledge merged intelligent mechanisms and algorithms
- Chinese Journal of Intelligent Science and Technology Vol. 2, Issue 2, Pages: 101-106(2020)
- 作者机构：
  
  1. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
  2. 中国科学院大学，北京，100049
  3. 青岛智能产业技术研究院，山东青岛 266109
  4. 滑铁卢大学，加拿大滑铁卢 N2L 3G1
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202011
  CLC： TP301
- Published Online：2020-06，
  
  Published：20 June 2020
- 稿件说明：
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Fei-Yue WANG, Dongpu CAO, Qinglai WEI. Reinforcement learning:toward action-knowledge merged intelligent mechanisms and algorithms[J]. Chinese journal of intelligent science and technology, 2020, 2(2): 101-106.
DOI：

Fei-Yue WANG, Dongpu CAO, Qinglai WEI. Reinforcement learning:toward action-knowledge merged intelligent mechanisms and algorithms[J]. Chinese journal of intelligent science and technology, 2020, 2(2): 101-106. DOI： 10.11959/j.issn.2096-6652.202011.

摘要

简要地评论了强化学习的历史、现状与未来的发展途径，认为强化学习应从先行后知、先知后行向知行合一的平行强化学习迈进，实现在虚拟世界“吃一堑”，在物理世界“长一智”，真正成为智慧机制和智能算法的基础学习理论。

Abstract

This article discusses briefly the history

the state of the art and the future of reinforcement learning

and outlines a roadmap of evolution from learning by doing

doing with planning to parallel intelligence that combining learning virtually in artificial systems and acting accordingly in actual systems.

关键词

Keywords

references

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