As open-ended learning based on divergent search algorithms such as Novelty Search (NS) draws more and more attention from the research community, it is natural to expect that its application to increasingly complex real-world problems will require the exploration to operate in higher dimensional Behavior Spaces which will not necessarily be Euclidean. Novelty Search traditionally relies on k-nearest neighbours search and an archive of previously visited behavior descriptors which are assumed to live in a Euclidean space. This is problematic because of a number of issues. On one hand, Euclidean distance and Nearest-neighbour search are known to behave differently and become less meaningful in high dimensional spaces. On the other hand, the archive has to be bounded since, memory considerations aside, the computational complexity of finding nearest neighbours in that archive grows linearithmically with its size. A sub-optimal bound can result in "cycling" in the behavior space, which inhibits the progress of the exploration. Furthermore, the performance of NS depends on a number of algorithmic choices and hyperparameters, such as the strategies to add or remove elements to the archive and the number of neighbours to use in k-nn search. In this paper, we discuss an alternative approach to novelty estimation, dubbed Behavior Recognition based Novelty Search (BR-NS), which does not require an archive, makes no assumption on the metrics that can be defined in the behavior space and does not rely on nearest neighbours search. We conduct experiments to gain insight into its feasibility and dynamics as well as potential advantages over archive-based NS in terms of time complexity.
翻译:由于基于诸如Novellty Search(NS)等不同搜索算法的开放学习,引起了研究界越来越多的关注,因此自然地期望,在日益复杂的现实世界问题中应用这种算法将要求探索在较高级行为空间中操作,而这种空间不一定是Euclidean。新发现搜索传统上依赖于K-最近邻居的搜索和以前访问过的行为描述器的档案,假定它们生活在Euclidean 空间中。这有问题。一方面,远距离和近邻搜索在研究界中越来越引起越来越多的关注。一方面,人们知道Euclidean 距离和近距离搜索对于日益复杂的现实世界问题的应用,在高度空间空间空间空间空间问题中,其作用将变得不同,变得不太有意义。另一方面,档案必须被束缚起来,因为除了记忆考虑之外,在档案库中找到最近的邻居的计算复杂性随其大小而增长。亚优度的束缚可以导致在行为空间中“循环”,这抑制了探索的进展。此外,NS的运行状况取决于一定的算法选择数量和超近距离的预测,而在高空间空间空间空间的假设中并不需要搜索,在Klibalbalbal-real-rial-real-regiew-real-reme-reme-redududustral-我们使用这种战略,我们不使用这种战略,因此不需要在纸路的搜索的搜索到纸路路面的计算方法,因此需要增加或缩到纸路路路路面的搜索。