强化学习向光连接资料
2016-04-12 16:54
651 查看
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
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。
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)
http://rll.berkeley.edu/deeprlworkshop/
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-papershttps://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 2015http://rll.berkeley.edu/deeprlworkshop/
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