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(zhuan) Deep Reinforcement Learning Papers

Deep Reinforcement Learning Papers A list of recent papers regarding deep reinforcement learning. The papers are organized based on manually-defined bookmarks. They are sorted by time to see the recent papers first. Any suggestions and pull requests…

(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…

(转) Deep Reinforcement Learning: Pong from Pixels

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…

论文笔记之：Active Object Localization with Deep Reinforcement Learning

Active Object Localization with Deep Reinforcement Learning ICCV 2015 最近Deep Reinforcement Learning算是火了一把,在Google Deep Mind的主页上,更是许多关于此的paper,基本都发在ICML,AAAI,IJCAI等各种人工智能,机器学习的牛会顶刊,甚至是Nature,可以参考其官方publication page: https://www.deepmind.com/publicatio…

[DQN] What is Deep Reinforcement Learning

已经成为DL中专门的一派,高大上的样子 Intro: MIT 6.S191 Lecture 6: Deep Reinforcement Learning Course: CS 294: Deep Reinforcement Learning Jan 18: Introduction and course overview (Levine, Finn, Schulman) Slides: Levine Slides: Finn Slides: Schulman Video Why deep rei…

论文笔记：Learning how to Active Learn: A Deep Reinforcement Learning Approach

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…

论文阅读之: Hierarchical Object Detection with Deep Reinforcement Learning

Hierarchical Object Detection with Deep Reinforcement Learning NIPS 2016 WorkShop  Paper : https://arxiv.org/pdf/1611.03718v1.pdf Project Page : https://github.com/imatge-upc/detection-2016-nipsws  摘要: 我们提出一种基于深度强化学习的等级物体检测方法 (Hierarchical Object  De…

Deep Reinforcement Learning 基础知识

Introduction 深度增强学习Deep Reinforcement Learning是将深度学习与增强学习结合起来从而实现从Perception感知到Action动作的端对端学习的一种全新的算法.简单的说,就是和人类一样,输入感知信息比如视觉,然后通过深度神经网络,直接输出动作,中间没有hand-crafted工作.深度增强学习具备使机器人实现完全自主的学习一种甚至多种技能的潜力. 虽然将深度学习和增强学习结合的想法在几年前就有人尝试,但真正成功的开端是DeepMind在NIPS 201…

Deep Reinforcement Learning 基础知识（DQN方面）

Introduction 深度增强学习Deep Reinforcement Learning是将深度学习与增强学习结合起来从而实现从Perception感知到Action动作的端对端学习的一种全新的算法.简单的说,就是和人类一样,输入感知信息比如视觉,然后通过深度神经网络,直接输出动作,中间没有hand-crafted工作.深度增强学习具备使机器人实现完全自主的学习一种甚至多种技能的潜力. 虽然将深度学习和增强学习结合的想法在几年前就有人尝试,但真正成功的开端是DeepMind在NIPS 201…

深度强化学习（Deep Reinforcement Learning）入门：RL base & DQN-DDPG-A3C introduction

转自https://zhuanlan.zhihu.com/p/25239682 过去的一段时间在深度强化学习领域投入了不少精力,工作中也在应用DRL解决业务问题.子曰:温故而知新,在进一步深入研究和应用DRL前,阶段性的整理下相关知识点.本文集中在DRL的model-free方法的Value-based和Policy-base方法,详细介绍下RL的基本概念和Value-based DQN,Policy-based DDPG两个主要算法,对目前state-of-art的算法(A3C)详细介绍,其他…