Reinforcement Learning in R

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Reinforcement Learning in R

Reinforcement learning has gained considerable traction as it mines real experiences with the help of trial-and-error learning to model decision-making. Thus, this approach attempts to imitate the fundamental method used by humans of learning optimal…

Awesome Reinforcement Learning

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…

Machine Learning Algorithms Study Notes(5)—Reinforcement Learning

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…

(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 Learning Research Review Week 2: Reinforcement Learning

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…

论文笔记之：Asynchronous Methods for Deep Reinforcement Learning

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…

论文笔记之：Playing Atari with Deep Reinforcement Learning

Playing Atari with Deep Reinforcement Learning <Computer Science>, 2013 Abstract: 本文提出了一种深度学习方法,利用强化学习的方法,直接从高维的感知输入中学习控制策略.模型是一个卷积神经网络,利用 Q-learning的一个变种来进行训练,输入是原始像素,输出是预测将来的奖励的 value function.将此方法应用到 Atari 2600 games 上来,进行测试,发现在所有游戏中都比之前的方法有效,甚至在…

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

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增强学习（Reinforcement Learning and Control）

增强学习(Reinforcement Learning and Control) [pdf版本]增强学习.pdf 在之前的讨论中,我们总是给定一个样本x,然后给或者不给label y.之后对样本进行拟合.分类.聚类或者降维等操作.然而对于很多序列决策或者控制问题,很难有这么规则的样本.比如,四足机器人的控制问题,刚开始都不知道应该让其动那条腿,在移动过程中,也不知道怎么让机器人自动找到合适的前进方向. 另外如要设计一个下象棋的AI,每走一步实际上也是一个决策过程,虽然对于简单的棋有A*的启发式…

Deep Reinforcement Learning for Dialogue Generation 论文阅读

本文来自李纪为博士的论文 Deep Reinforcement Learning for Dialogue Generation. 1,概述当前在闲聊机器人中的主要技术框架都是seq2seq模型.但传统的seq2seq存在很多问题.本文就提出了两个问题: 1)传统的seq2seq模型倾向于生成安全,普适的回答,例如“I don’t know what you are talking about”.为了解决这个问题,作者在更早的一篇文章中提出了用互信息作为模型的目标函数.具体见A Diversi…