深度强化学习策略梯度教程
深度强化学习
·
Deep Reinforcement Learning via Policy Optimization
成为VIP会员查看完整内容
相关内容
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
MIT新书《强化学习与最优控制》，REINFORCEMENT LEARNING AND OPTIMAL CONTROL https://web.mit.edu/dimitrib/www/Slides_Lecture13_RLOC.pdf https://web.mit.edu/dimitrib/www/RLbook.html
成为VIP会员查看完整内容
相关主题
相关VIP内容
相关资讯
相关论文
Prithviraj Ammanabrolu,Mark O. Riedl
4+阅读 · 2019年3月25日
Akash Mittal,Anuj Dhawan,Sourav Medya,Sayan Ranu,Ambuj Singh
8+阅读 · 2019年3月8日
Jesse Farebrother,Marlos C. Machado,Michael Bowling
5+阅读 · 2019年1月30日
Tuomas Haarnoja,Aurick Zhou,Sehoon Ha,Jie Tan,George Tucker,Sergey Levine
6+阅读 · 2018年12月26日
Borja Ibarz,Jan Leike,Tobias Pohlen,Geoffrey Irving,Shane Legg,Dario Amodei
4+阅读 · 2018年11月15日
Tom Zahavy,Matan Haroush,Nadav Merlis,Daniel J. Mankowitz,Shie Mannor
4+阅读 · 2018年9月6日
Iulian Vlad Serban,Chinnadhurai Sankar,Michael Pieper,Joelle Pineau,Yoshua Bengio
9+阅读 · 2018年7月12日
Seyed Sajad Mousavi,Michael Schukat,Enda Howley
12+阅读 · 2018年6月23日
Matthias Plappert,Rein Houthooft,Prafulla Dhariwal,Szymon Sidor,Richard Y. Chen,Xi Chen,Tamim Asfour,Pieter Abbeel,Marcin Andrychowicz
3+阅读 · 2018年1月31日