郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! 1 DeepMind, London, UK2 University College London, London, UK3 Princeton University, Princeton, NJ, USA*Correspondence: botvinick@google.com (M. Botvinick). Trends in Cognitive Sciences, May 2019, Vol. 23, No. 5 https:/…

Andrej Karpathy blog About Hacker's guide to Neural Networks Deep Reinforcement Learning: Pong from Pixels May 31, 2016 This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatica…

Introduction to Learning to Trade with Reinforcement Learning http://www.wildml.com/2018/02/introduction-to-learning-to-trade-with-reinforcement-learning/ Thanks a lot to @aerinykim, @suzatweet and @hardmaru for the useful feedback! The academic Deep…

http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/ The academic Deep Learning research community has largely stayed away from the financial markets. Maybe that’s because the finance industry has a bad reputation,…

  Deep Learning Research Review Week 2: Reinforcement Learning 转载自: https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-2-Reinforcement-Learning This is the 2nd installment of a new series called Deep Learning Resea…

Deep Learning in a Nutshell: Reinforcement Learning   Share: Posted on September 8, 2016by Tim Dettmers No CommentsTagged Deep Learning, Deep Neural Networks, Machine Learning,Reinforcement Learning This post is Part 4 of the Deep Learning in a Nutsh…

Awesome Reinforcement Learning A curated list of resources dedicated to reinforcement learning. We have pages for other topics: awesome-rnn, awesome-deep-vision, awesome-random-forest Maintainers: Hyunsoo Kim, Jiwon Kim We are looking for more contri…

Sutton 出版论文的主页: http://incompleteideas.net/publications.html Phd  论文:   temporal credit assignment in reinforcement learning http://incompleteideas.net/publications.html#PhDthesis 最近在做强化学习方面的课题, 发现在强化学习方面被称作强化学习之父的  Sutton  确实很厉害, TD算法和策略梯度策略算法都是他所提出…

深度强化学习的18个关键问题 from: https://zhuanlan.zhihu.com/p/32153603 85 人赞了该文章 深度强化学习的问题在哪里?未来怎么走?哪些方面可以突破? 这两天我阅读了两篇篇猛文A Brief Survey of Deep Reinforcement Learning 和 Deep Reinforcement Learning: An Overview ,作者排山倒海的引用了200多篇文献,阐述强化学习未来的方向.原文归纳出深度强化学习中的常见科学问题,…

CVPR2018_Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning http://mmlab.ie.cuhk.edu.hk/projects/RL-Restore/ 强化学习的入门介绍:https://blog.csdn.net/aliceyangxi1987/article/details/73327378 https://www.zhihu.com/question/41775291 CNN在l…