Deep Reinforcement Learning 深度增强学习资源
2016-08-23 15:49
489 查看
http://blog.csdn.net/songrotek/article/details/50572935
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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Deep Reinforcement Learning 深度增强学习资源 (持续更新)
Flood Sung
· 3 个月前
Deep Reinforcement Learning深度增强学习可以说发源于2013年DeepMind的Playing Atari with Deep Reinforcement Learning 一文,之后2015年DeepMind 在Nature上发表了Human Level Control through Deep Reinforcement Learning一文使Deep Reinforcement Learning得到了较广泛的关注,在2015年涌现了较多的Deep Reinforcement
Learning的成果。而2016年,随着AlphaGo的出现,Deep Reinforcement Learning 将进入全面发展的阶段。
Deep Reinforcement Learning面向决策与控制问题,而决策与控制很大程度上决定了人工智能的发展水平。也因此,AlphaGo的出现具有里程碑的意义。Deep Reinforcement Learning研究使用深度神经网络来解决决策控制问题,是深度学习领域最前沿的研究方向之一。
本文主要收集与Deep Reinforcement Learning相关的各种资料,希望对有兴趣研究的童鞋有所帮助。接下来,本专栏将由我继续发布Deep Reinforcement Learning的相关文章。
PS:最新的资料会在资料前方标出。
1 学习资料
1)增强学习相关课程:
David Silver的增强学习课程(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:Sutton & Barto Book: Reinforcement Learning: AnIntroduction
Nando de Freitas的深度学习课程 (有视频有ppt有作业):Machine
Learning
Michael Littman的增强学习课程:https://www.udacity.com/course/reinforcement-learning–ud600
Pieter Abbeel 的AI课程(包含增强学习,使用Pacman实验):Artificial
Intelligence
Pieter Abbeel 的深度增强学习课程:CS 294 Deep Reinforcement Learning, Fall 2015
Pieter Abbeel 的 高级机器人技术(Advanced Robotics):
CS287 Fall 2015
最新机器人专题课程Penn(2016年开课):Specialization
2)深度学习相关课程:
Fei Fei Li的用于视觉识别的卷积神经网络 :CS231n Convolutional Neural Networks for Visual Recognition
Andrew Ng(一个是Coursera上的课程,一个是Stanford的课程):Machine LearningCS
229: Machine Learning
Hinton的神经网络课程(Neural Network for Machine Learning)(2012年的)Coursera - Free Online Courses
From Top Universities
3)深度增强学习相关blog:
drl的入门博客(感谢知友Richard Huang)1.Guest Post (Part I): Demystifying Deep Reinforcement Learning
2.Guest Post (Part II): Deep Reinforcement Learning with Neon
3.Blog Post (Part III): Deep Reinforcement Learning with OpenAI Gym
(最新)Andrej Karpathy blog:
Deep Reinforcement Learning: Pong from Pixels
2 深度增强学习相关讲座
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
Deep Reinforcement Learning
(最新)ICML 2016:深度增强学习TutorialAlphaGo
Tutorial
Pieter Abbeel: https://www.youtube.com/watch?v=evq4p1zhS7Q (最新)Sergey Levine:
Deep Robotic Learning
(最新)John Schulman:Machine Learning Summer School
3 论文资料
GitHub - junhyukoh/deep-reinforcement-learning-papers: A list ofrecent papers regarding deep reinforcement learning
GitHub - muupan/deep-reinforcement-learning-papers: A list of papers
and resources dedicated to deep reinforcement learning
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。
4 大牛与企业情况:
DeepMind:http://www.deepmind.com/publications.htmlOpenAI:
OpenAI Gym
Pieter Abbeel 团队(已加入OpenAI):Pieter Abbeel---Associate Professor UC Berkeley---Co-Founder
Gradescope---
Satinder Singh:Home page for Satinder Singh (Baveja) and Reinforcement Learning
CMU 进展:Lerrel PintoRuslan
Salakhutdinov
Prefered Networks: (日本创业公司)Preferred Networks
Osaro:www.osaro.com
5 会议情况
NIPS 2015 Deep Reinforcement Learning WorkshopICLR 2016
(最新)RSS 2016 Deep Learning for Robotics
6 开源代码
在github可以找到dqn,ddpg,a3c, trpo 等深度增强学习典型算法的代码,以下为一些举例的开源代码:GitHub - songrotek/DeepTerrainRL: terrain-adaptive locomotion skills using deep reinforcement
learning
GitHub - songrotek/async-rl: An attempt to reproduce the results of "Asynchronous Methods for
Deep Reinforcement Learning" (http://arxiv.org/abs/1602.01783)
GitHub - songrotek/rllab: rllab is a framework for developing and evaluating reinforcement learning
algorithms.
GitHub - songrotek/DRL-FlappyBird: Playing Flappy Bird Using Deep Reinforcement Learning
(Based on Deep Q Learning DQN using Tensorflow)
GitHub - songrotek/DeepMind-Atari-Deep-Q-Learner: The original code from
the DeepMind article + my tweaks
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