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Title: Hindsight Experience Replay | Keavnn'Blog

Open Graph Title: Hindsight Experience Replay

X Title: Hindsight Experience Replay

Description: 本文介绍了一个“事后诸葛亮”的经验池机制，简称为HER，它可以很好地应用于稀疏奖励和二分奖励的问题中，不需要复杂的奖励函数工程设计。 推荐： 稀疏奖励问题的一种解决方案 通俗易懂

Open Graph Description: 本文介绍了一个“事后诸葛亮”的经验池机制，简称为HER，它可以很好地应用于稀疏奖励和二分奖励的问题中，不需要复杂的奖励函数工程设计。 推荐： 稀疏奖励问题的一种解决方案 通俗易懂

X Description: 本文介绍了一个“事后诸葛亮”的经验池机制，简称为HER，它可以很好地应用于稀疏奖励和二分奖励的问题中，不需要复杂的奖励函数工程设计。 推荐： 稀疏奖励问题的一种解决方案 通俗易懂

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Opengraph URL: http://StepNeverStop.github.io/Hindsight-Experience-Replay.html

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搜索 javascript:;
ReinforcementLearning https://stepneverstop.github.io/categories/ReinforcementLearning/
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#简介
https://papers.nips.cc/paper/7090-hindsight-experience-replay.pdfhttps://papers.nips.cc/paper/7090-hindsight-experience-replay.pdf
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#二分奖励-binary-reward
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#稀疏奖励-sparse-reward
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#文中精要
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#HER
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#伪代码
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#HER的优点
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#实验部分
https://goo.gl/SMrQnIhttps://goo.gl/SMrQnI
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#环境
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#算法
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#训练结果
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#final模式与future模式对比
count-basedhttps://arxiv.org/pdf/1703.01310.pdf
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#单个目标状态的实验
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#HER应用于reward-shaping问题中
https://stepneverstop.github.io/Hindsight-Experience-Replay.html#四种模式比较
Hindsight Experience Replayhttps://stepneverstop.github.io/Hindsight-Experience-Replay.html
Keavnnhttps://stepneverstop.github.io/
http://StepNeverStop.github.io/Hindsight-Experience-Replay.htmlhttps://stepneverstop.github.io/Hindsight-Experience-Replay.html
署名-非商业性使用-相同方式共享 4.0 国际https://creativecommons.org/licenses/by-nc-sa/4.0/
rlhttps://stepneverstop.github.io/tags/rl/
Prioritized Experience Replay https://stepneverstop.github.io/Prioritized-Experience-Replay.html
Energy-Based Hindsight Experience Prioritization https://stepneverstop.github.io/energy-based-hindsight-experience-prioritization.html
51 日志 https://stepneverstop.github.io/archives/
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1. 简介https://stepneverstop.github.io/Hindsight-Experience-Replay.html#简介
1.1. 二分奖励 binary rewardhttps://stepneverstop.github.io/Hindsight-Experience-Replay.html#二分奖励-binary-reward
1.2. 稀疏奖励 sparse rewardhttps://stepneverstop.github.io/Hindsight-Experience-Replay.html#稀疏奖励-sparse-reward
2. 文中精要https://stepneverstop.github.io/Hindsight-Experience-Replay.html#文中精要
2.1. HERhttps://stepneverstop.github.io/Hindsight-Experience-Replay.html#HER
2.2. 伪代码https://stepneverstop.github.io/Hindsight-Experience-Replay.html#伪代码
2.3. HER的优点https://stepneverstop.github.io/Hindsight-Experience-Replay.html#HER的优点
3. 实验部分https://stepneverstop.github.io/Hindsight-Experience-Replay.html#实验部分
3.1. 环境https://stepneverstop.github.io/Hindsight-Experience-Replay.html#环境
3.2. 算法https://stepneverstop.github.io/Hindsight-Experience-Replay.html#算法
3.3. 训练结果https://stepneverstop.github.io/Hindsight-Experience-Replay.html#训练结果
3.3.1. final模式与future模式对比https://stepneverstop.github.io/Hindsight-Experience-Replay.html#final模式与future模式对比
3.3.2. 单个目标状态的实验https://stepneverstop.github.io/Hindsight-Experience-Replay.html#单个目标状态的实验
3.3.3. HER应用于reward shaping问题中https://stepneverstop.github.io/Hindsight-Experience-Replay.html#HER应用于reward-shaping问题中
3.3.4. 四种模式比较https://stepneverstop.github.io/Hindsight-Experience-Replay.html#四种模式比较

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