Bayesian Optimization for Contextual Policy Search *
Bayesian Optimization for Contextual Policy Search *
复制标题
上下文策略搜索的贝叶斯优化*
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Jonas Hansen
中科院分区：
文献类型：
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作者：
J. H. Metzen;Alexander Fabisch;Jonas Hansen
上下文策略搜索允许根据不同的情况调整机器人的运动原语。例如，运动原语可能适应不同的地形倾斜度或期望的行走速度。这种适应通常可以通过修改相对少量的超参数来实现；然而，在实际的机器人系统上进行的学习通常仅限于相对少量的试验。在黑盒优化中，贝叶斯优化是一种流行的全局搜索方法，用于解决低维搜索空间但代价函数昂贵的问题。我们将贝叶斯优化扩展到上下文策略搜索。初步结果表明，贝叶斯优化在低维上下文策略搜索问题上优于局部搜索方法。
Contextual policy search allows adapting robotic movement primitives to different situations. For instance, a locomotion primitive might be adapted to different terrain inclinations or desired walking speeds. Such an adaptation is often achievable by modifying a relatively small number of hyperparameters; however, learning when performed on an actual robotic system is typically restricted to a relatively small number of trials. In black-box optimization, Bayesian optimization is a popular global search approach for addressing such problems with low-dimensional search space but expensive cost function. We present an extension of Bayesian optimization to contextual policy search. Preliminary results suggest that Bayesian optimization outperforms local search approaches on low-dimensional contextual policy search problems.