论文标题
使用K-均值的有效搜索主动推理策略空间
Efficient search of active inference policy spaces using k-means
论文作者
论文摘要
我们开发了一种主动推论中的策略选择方法，该方法使我们能够通过将每个策略映射到矢量空间中的嵌入来有效地搜索大型策略空间。我们采样了空间中代表点的预期自由能，然后在此初始样本中最有希望的点进行更彻底的策略搜索。我们考虑了创建策略嵌入空间的各种方法，并建议使用K-均值聚类选择代表点。我们将技术应用于面向目标的图形 - 传播问题，对于中等大图，天真的策略选择也很棘手。
We develop an approach to policy selection in active inference that allows us to efficiently search large policy spaces by mapping each policy to its embedding in a vector space. We sample the expected free energy of representative points in the space, then perform a more thorough policy search around the most promising point in this initial sample. We consider various approaches to creating the policy embedding space, and propose using k-means clustering to select representative points. We apply our technique to a goal-oriented graph-traversal problem, for which naive policy selection is intractable for even moderately large graphs.
