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! Abstract 在中脑多巴胺能神经元的研究中取得了许多最新进展.要了解这些进步以及它们之间的相互关系,需要对作为解释框架并指导正在进行的实验探究的计算模型有深刻的理解.现在,理论和实验的这种相互交织非常清楚地表明,中脑多巴胺神经元的阶段性活动为突触改变提供了一个整体机制.这些突触改变反过来又为特定类别的强化学习机制提供了机械基础,而强化学习机制现在似乎已成为人类和动物行为的基础.这篇综述既描述了该结论的关键经验性发现,也描述了得出此…

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! Contents: Abstract 1. Introduction 2. Reward-Prediction Error Meets Dopamine 3. Reward-Prediction Error and Incentive Salience: What Do They Explain? 4. Explanatory Depth, Reward-Prediction Error and Incentive Salience…

最近在在学习强化学习方面的东西, 对于现有的很多文章中关于强化学习的知识很是不理解,很多都是一个公式套一个公式,也没有什么太多的解释,感觉像是在看天书一般,经过了较长时间的挣扎最后决定从一些基础的东西开始入手,于是便有了这篇论文的发现. Learning  from  Delayed  Reward    该论文的页面为:   http://www.cs.rhul.ac.uk/~chrisw/thesis.html 下载地址为:            http://www.cs.rhul.ac.…

Byte Tank Posts Archive Deep Reinforcement Learning: Playing a Racing Game OCT 6TH, 2016 Agent playing Out Run, session 201609171218_175epsNo time limit, no traffic, 2X time lapse Above is the built deep Q-network (DQN) agent playing Out Run, trained…

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…

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…

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,…

https://www.zhihu.com/question/277325426 https://github.com/jinglescode/reinforcement-learning-tic-tac-toe/blob/master/README.md Intuition After a long day at work, you are deciding between 2 choices: to head home and write a Medium article or hang o…

Reinforcement Learning 对于控制决策问题的解决思路:设计一个回报函数(reward function),如果learning agent(如上面的四足机器人.象棋AI程序)在决定一步后,获得了较好的结果,那么我们给agent一些回报(比如回报函数结果为正),得到较差的结果,那么回报函数为负.比如,四足机器人,如果他向前走了一步(接近目标),那么回报函数为正,后退为负.如果我们能够对每一步进行评价,得到相应的回报函数,那么就好办了,我们只需要找到一条回报值最大的路径(每步的回…