wxmcp3的博客

进化策略的视觉指南 A Visual Guide to Evolution Strategiesbrief博客地址 链接这个图首先就好有趣！Survival of the fittest.物竞天择，适者生存下面开始正文：在这篇文章中，我借助一些直观的例子来解释进化策略（ES）是如何工作的。我尽量让方程式保持轻松，如果读者希望了解更多细节，我提供了原始文章的链接。这是一系列文章的第一篇，我计划在这里展示如何将这些算法应用到从MNIST、OpenAI Gym、Roboschool到PyBullet

Policy Search in Continuous Action Domains: an OverviewAbstractContinuous action policy search is currently the focus of intensive research, driven both by the recent success of deep reinforcement learning algorithms and the emergence of competitors base

Evolution Strategies as a Scalable Alternative to Reinforcement Learningbrief文章链接该文章是 Open AI 17年发布的，目前有300+的引用量。Abstract【开篇明意】We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular

Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking AgentsBrief目前该文已经有了上百的引用量，还是有点厉害。文章地址 链接代码链接 code作者来自佛罗里达大学和openAIAbstract【开篇明义】Evolution strategies (ES) are a family of black-box opt

Benchmarking Deep Reinforcement Learning for Continuous ControlBriefpaper 链接paper开源代码code引用量 300++作者Pieter Abbeel来自UC BerkeleyAbstract鉴于RL领域具有很强的时效性，这篇2016年开源的论文在当时应该是非常轰动的。不过时至今日，...

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftBrief该论文是关于 BN， 引用量300+论文链接paperPieter Abbeel Sergey Levine 作者依然是两位大佬

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic ActorBrief文章链接paper代码链接code作者Sergey Levine来自加州伯克利分校UC BerkeleyAbstract

Evolution-Guided Policy Gradient in Reinforcement LearningBrief文章链接 paper代码链接code作者 Kagan Tumer作者 另外一个作者是因特尔人工智能研究院的。Intel AI · Ai-Lab俄勒冈州立大学协作机器人与智能系统研究所Collaborative Robotics and Intelligent Systems Institute Oregon State UniversityAbstract文章是在.

【文章阅读】Incremental Reinforcement Learning in Continuous Spaces via Policy Relaxation and Importance WeightingBriefAbstractIntroduction第一段：第二段：第三段：第四段：第五段：第六段：第七段：BackgroundA. Reinforcement Learning in Continuous Spaces连续空间强化学习B. Related Work相关工作第三章方法设计INCRE