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 我们提出了一种基于情绪的分层强化学习(HRL)算法,用于具有多种奖励来源的环境.该系统的架构受到大脑神经生物学的启发,特别是负责情绪,决策和行为执行的区域,分别是杏仁核,眶额皮质和基底神经节.学习问题根据奖励的来源而分解.奖励源用作给定子任务的目标.为每个子任务分配了一个人工情绪指示(AEI),该AEI可预测与该子任务相关的奖励成分.同时学习AEI和顶层策略,并在AEI发生重大变化时中断子任务的执行.该算法在具有两个…
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition ICCV 2019 (oral) 2019-08-01 15:08:19 Paper:https://arxiv.org/abs/1907.13369 1. Backgroud and Motivation: 本文提出一种基于多智能体强化学习的未裁剪视频识别模型,来自适应的从未裁剪视频中,截取出样本视频…
Deep Reinforcement Learning Based Trading Application at JP Morgan Chase https://medium.com/@ranko.mosic/reinforcement-learning-based-trading-application-at-jp-morgan-chase-f829b8ec54f2 FT released a story today about the new application that will op…
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.…
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…
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…
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…
Evolution Strategies as a Scalable Alternative to Reinforcement Learning this blog from: https://blog.openai.com/evolution-strategies/   MARCH 24, 2017 Evolution Strategies as a Scalable Alternative to Reinforcement Learning We’ve discovered that evo…
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…