Asynchronous Methods for Deep Reinforcement Learning ICML 2016 深度强化学习最近被人发现貌似不太稳定,有人提出很多改善的方法,这些方法有很多共同的 idea:一个 online 的 agent 碰到的观察到的数据序列是非静态的,然后就是,online的 RL 更新是强烈相关的.通过将 agent 的数据存储在一个 experience replay 单元中,数据可以从不同的时间步骤上,批处理或者随机采样.这种方法可以降低 non-st…

Playing FPS games with deep reinforcement learning 博文转自:https://blog.acolyer.org/2016/11/23/playing-fps-games-with-deep-reinforcement-learning/ When I wrote up 'Asynchronous methods for deep learning' last month, I made a throwaway remark that after…

  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…

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…

Applications of Reinforcement Learning in Real World 2018-08-05 18:58:04 This blog is copied from: https://towardsdatascience.com/applications-of-reinforcement-learning-in-real-world-1a94955bcd12 There is no reasoning, no process of inference or comp…

Learning how to Active Learn: A Deep Reinforcement Learning Approach 2018-03-11 12:56:04 1. Introduction: 对于大部分 NLP 的任务,得到足够的标注文本来进行模型的训练是一个关键的瓶颈.所以,active learning 被引入到 NLP 任务中以最小化标注数据的代价.AL 的目标是通过识别一小部分数据来进行标注,以此来降低 cost,选来最小化监督模型的精度. 毫无疑问的是,AL 对于其…

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,…

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…