相关内容

【导读】来自Jordi Pons一份循环神经网络RNNs简明教程,37页ppt

成为VIP会员查看完整内容
RNNsTutorial.pdf
0
104

The tutorial is written for those who would like an introduction to reinforcement learning (RL). The aim is to provide an intuitive presentation of the ideas rather than concentrate on the deeper mathematics underlying the topic. RL is generally used to solve the so-called Markov decision problem (MDP). In other words, the problem that you are attempting to solve with RL should be an MDP or its variant. The theory of RL relies on dynamic programming (DP) and artificial intelligence (AI). We will begin with a quick description of MDPs. We will discuss what we mean by “complex” and “large-scale” MDPs. Then we will explain why RL is needed to solve complex and large-scale MDPs. The semi-Markov decision problem (SMDP) will also be covered.

The tutorial is meant to serve as an introduction to these topics and is based mostly on the book: “Simulation-based optimization: Parametric Optimization techniques and reinforcement learning” [4]. The book discusses this topic in greater detail in the context of simulators. There are at least two other textbooks that I would recommend you to read: (i) Neuro-dynamic programming [2] (lots of details on convergence analysis) and (ii) Reinforcement Learning: An Introduction [11] (lots of details on underlying AI concepts). A more recent tutorial on this topic is [8]. This tutorial has 2 sections: • Section 2 discusses MDPs and SMDPs. • Section 3 discusses RL. By the end of this tutorial, you should be able to • Identify problem structures that can be set up as MDPs / SMDPs. • Use some RL algorithms.

成为VIP会员查看完整内容
0
69
小贴士
相关主题
相关VIP内容
专知会员服务
67+阅读 · 2020年8月4日
专知会员服务
75+阅读 · 2020年8月2日
专知会员服务
66+阅读 · 2020年7月14日
专知会员服务
45+阅读 · 2020年6月8日
Fariz Darari简明《博弈论Game Theory》介绍,35页ppt
专知会员服务
63+阅读 · 2020年5月15日
一份循环神经网络RNNs简明教程,37页ppt
专知会员服务
104+阅读 · 2020年5月6日
专知会员服务
99+阅读 · 2020年2月1日
强化学习最新教程,17页pdf
专知会员服务
69+阅读 · 2019年10月11日
相关论文
Tuomas Haarnoja,Aurick Zhou,Sehoon Ha,Jie Tan,George Tucker,Sergey Levine
6+阅读 · 2018年12月26日
Ziwei Zhang,Peng Cui,Wenwu Zhu
40+阅读 · 2018年12月11日
Global Deep Learning Methods for Multimodality Isointense Infant Brain Image Segmentation
Zhengyang Wang,Na Zou,Dinggang Shen,Shuiwang Ji
3+阅读 · 2018年12月10日
Jingkang Wang,Yang Liu,Bo Li
3+阅读 · 2018年10月5日
Michael Thoreau,Navinda Kottege
7+阅读 · 2018年6月20日
Tambet Matiisen,Aqeel Labash,Daniel Majoral,Jaan Aru,Raul Vicente
4+阅读 · 2018年5月21日
Sham Kakade,Mengdi Wang,Lin F. Yang
3+阅读 · 2018年4月25日
Mohammadhosein Hasanbeig,Alessandro Abate,Daniel Kroening
5+阅读 · 2018年4月22日
Asli Celikyilmaz,Antoine Bosselut,Xiaodong He,Yejin Choi
5+阅读 · 2018年3月27日
Vincent Dumoulin,Francesco Visin
6+阅读 · 2018年1月11日
Top