Learning How to Actively Learn: A Deep Imitation Learning Approach

Learning How to Actively Learn: A Deep Imitation Learning Approach的相关文章

(转) Playing FPS games with deep reinforcement learning

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

论文笔记 Deep Patch Learning for Weakly Supervised Object Classi cation and Discovery

Background 1) "Patch-level image representation"的优势 "Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background" &

Deep Reinforcement Learning 深度增强学习资源

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/p

论文笔记之：Collaborative Deep Reinforcement Learning for Joint Object Search

Collaborative Deep Reinforcement Learning for Joint Object Search   CVPR 2017 Motivation: 传统的 bottom-up object region proposals 的方法,由于提取了较多的 proposal,导致后续计算必须依赖于抢的计算能力,如 GPU 等.那么,在计算机不足的情况下,则会导致应用范围受限.而 Active search method (就是 RL 的方法) 则提供了不错的方法,可以很大

Deep Active Learning 深度主动学习

Deep Active Learning 最上方为监督学习,对面为非监督学习,之间包括增强学习.半监督学习.在线学习.主动学习. Supervised Learing 将未标记的数据交给Work进行标记,然后将标记数据交给Learner进行训练. Semi-Supervised Learning 在监督学习的基础上加了一条线,也就是把大量的未标记数据和少量的标记数据交给Learner进行训练,这样可以减少人工标记的时间. Active Learning 通过Learner来决定哪些数据需要被标记

repost: Deep Reinforcement Learning

From: http://wanghaitao8118.blog.163.com/blog/static/13986977220153811210319/ accessed 2016-03-10 深度强化学习(Deep Reinforcement Learning)的资源 Google的Deep Mind团队2013年在NIPS上发表了一篇牛x闪闪的文章,亮瞎了好多人眼睛,不幸的是我也在其中.前一段时间收集了好多关于这方面的资料,一直躺在收藏夹中,目前正在做一些相关的工作(希望有小伙伴一起交流)

论文笔记之：Deep Reinforcement Learning with Double Q-learning

Deep Reinforcement Learning with Double Q-learning Google DeepMind Abstract 主流的 Q-learning 算法过高的估计在特定条件下的动作值.实际上,之前是不知道是否这样的过高估计是 common的,是否对性能有害,以及是否能从主体上进行组织.本文就回答了上述的问题,特别的,本文指出最近的 DQN 算法,的确存在在玩 Atari 2600 时会 suffer from substantial overestimation

(转) Deep Reinforcement Learning: Playing a Racing Game

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

论文笔记之： Deep Metric Learning via Lifted Structured Feature Embedding

Deep Metric Learning via Lifted Structured Feature Embedding CVPR 2016 摘要:本文提出一种距离度量的方法,充分的发挥 training batches 的优势,by lifting the vector of pairwise distances within the batch to the matrix of pairwise distances. 刚开始看这个摘要,有点懵逼,不怕,后面会知道这段英文是啥意思的. 引言部分