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目前DRL仍然存在的两大开放性问题：
Data inefficient and require a lot of data
Unpredictable and unstable
Reinforcement learning as inference soft optimality：RL agent
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搜索广告排序与竞价机制：淘宝的广告平台涉及三方利益，在对广告商进行相应收费的时候采取点击收费制度，并使用GSP二价扣费进行扣费计算，保证排序的同时让广告商以最低价出价。作者用policy optimization对排序公式进行优化，在仿真中对agent进行pretrain，之后在线学习使用evolu
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数据驱动的其根本需要与实验设计：数据驱动已经是行业共识，然而真正做到这个的企业非常少，真正见过的人更少。数据驱动：最重要两点数据/驱动。数据：全方位采集数据。驱动：让大家看数据，让所有人盯着数据。
根本需要：AB Test实验平台，对比试验。每次实验需要三个重要因素：流量，参数，结果。实验的结果往往
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<a href="/2019/03/19/推荐系统：常用指标（blog总结）/" class="post-title-link" itemprop="url">推荐系统：检测方法常用指标（blog总结）</a>
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<time title="创建时间：2019-03-19 15:41:09" itemprop="dateCreated datePublished" datetime="2019-03-19T15:41:09+08:00">2019-03-19</time>
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<span itemprop="about" itemscope="" itemtype="http://schema.org/Thing"><a href="/categories/推荐系统/" itemprop="url" rel="index"><span itemprop="name">推荐系统</span></a></span>
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常用检测方法设计完了系统并不代表就可以高枕无忧了，在系统上线后必须关注一定的指标，仔细地维护系统健康度。常用的推荐系统检测方法：
业务规则扫描
离线模拟测试
在线对比测试
用户访谈
业务规则扫描：运行单元测试，对推荐系统统一扫描业务规则。分为软硬两种，软扫描指在统计中触发少于一定概率，而硬就是一
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