DEEP LEARNING 大满贯课程表

Posted 2020-10-03 马兹

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Reinforcement Learning
post by ISH GIRWAN

Courses/Tutorials

Deep Reinforcement Learning, Spring 2017, by UC Berkeley: http://rll.berkeley.edu/deeprlcours...
Reinforcement Learning, 2015, by UCL (David Siver): http://www0.cs.ucl.ac.uk/staff/d.si...
https://github.com/yandexdataschool...
Lecture notes by Andrew Ng: http://cs229.stanford.edu/notes/cs2...
https://medium.com/emergent-future/...

Books

Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto: http://webdocs.cs.ualberta.ca/~sutt...

Blogs

http://bair.berkeley.edu/blog

I think you can take the UC Berkeley course instead of David Silver‘s course as it‘s more up to date. Additionally you can check Arthur Juliani‘s blog series, it‘s really good.

相关课程

Calculus One, Coursera, Jim Fowler
Calculus Two, Coursera, Jim Fowler
Multivariable Calculus, Khan Academy, Grant Sanderson
Linear Algebra, MIT, Prof. Gilbert Strang (so mechanical..)
Coding the Matrix, Brown University, Philip Klein
Introduction to Probability, The Science of Uncertainty Edx, MIT, Joh Tsitsiklis
微积分, coursera, 吉姆·福勒
微积分, coursera, 吉姆·福勒
多元微积分, 汗学院, grant sanderson
线性代数, 麻省理工学院教授吉尔伯特·斯特朗(所以机械..)
编码矩阵, 布朗大学, 菲利普·克莱因
介绍概率, 不确定的科学, 麻省理工学院, joh tsitsiklis

以下是比较旧的RL Course by David Silver

UCL Course on RL
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

Advanced Topics 2015 (COMPM050/COMPGI13)

Reinforcement Learning

Contact: [email protected]

Video-lectures available here

Lecture 1: Introduction to Reinforcement Learning

Lecture 2: Markov Decision Processes

Lecture 3: Planning by Dynamic Programming

Lecture 4: Model-Free Prediction

Lecture 5: Model-Free Control

Lecture 6: Value Function Approximation

Lecture 7: Policy Gradient Methods