1 学习资料

增强学习课程 David Silver (有视频和ppt):

http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching.html

最好的增强学习教材:

Reinforcement Learning: An Introduction

https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html

 

深度学习课程 (有视频有ppt有作业)

 

https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/

 

深度增强学习的讲座都是David Silver的:

ICLR 2015 part 1 https://www.youtube.com/watch?v=EX1CIVVkWdE

ICLR 2015 part 2 https://www.youtube.com/watch?v=zXa6UFLQCtg

UAI 2015 https://www.youtube.com/watch?v=qLaDWKd61Ig

RLDM 2015 http://videolectures.net/rldm2015_silver_reinforcement_learning/

 

其他课程:

增强学习

Michael Littman:

https://www.udacity.com/course/reinforcement-learning–ud600

 

AI(包含增强学习,使用Pacman实验)

Pieter Abbeel:

https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1x-0#.VKuKQmTF_og

 

Deep reinforcement Learning:

Pieter Abbeel

http://rll.berkeley.edu/deeprlcourse/

 

高级机器人技术(Advanced Robotics):

Pieter Abbeel:

http://www.cs.berkeley.edu/~pabbeel/cs287-fa15/

 

深度学习相关课程:

用于视觉识别的卷积神经网络(Convolutional Neural Network for visual network)

http://cs231n.github.io/

 

机器学习 Machine Learning

Andrew Ng

https://www.coursera.org/learn/machine-learning/

http://cs229.stanford.edu/

 

神经网络(Neural Network for Machine Learning)(2012年的)

Hinton:

https://www.coursera.org/course/neuralnets

 

最新机器人专题课程Penn(2016年开课):

https://www.coursera.org/specializations/robotics

 

2 论文资料

https://github.com/junhyukoh/deep-reinforcement-learning-papers

https://github.com/muupan/deep-reinforcement-learning-papers

 

这两个人收集的基本涵盖了当前deep reinforcement learning 的论文资料。目前确实不多。

 

3 大牛情况:

DeepMind:

http://www.deepmind.com/publications.html

 

Pieter Abbeel 团队:

http://www.eecs.berkeley.edu/~pabbeel/

 

Satinder Singh:

http://web.eecs.umich.edu/~baveja/

 

CMU 进展:

http://www.cs.cmu.edu/~lerrelp/

 

Prefered Networks: (日本创业公司,很强,某有代码)

 

4 会议情况

Deep Reinforcement Learning Workshop NIPS 2015

http://rll.berkeley.edu/deeprlworkshop/

http://blog.csdn.net/songrotek/article/details/50572935