在语义空间中搜索程序化策略
Searching for Programmatic Policies in Semantic Spaces
AI总结：
本文提出在程序语义空间而非语法空间中进行策略合成搜索，通过学习行为多样的程序库并定义邻域函数实现，在MicroRTS中验证了该方法具有更高的样本效率。
摘要：
语法引导合成通常用于生成编码策略的程序。在该方法中，可以用领域特定语言编写的程序集合定义了搜索空间，算法在此空间内搜索编码强策略的程序。本文提出了一种合成程序化策略的替代方法，我们在语言语义空间的近似中进行搜索。我们假设在语义空间中搜索比基于语法的空间更具样本效率。我们的理由是，如果算法在搜索空间时评估不同的智能体行为，搜索会更高效，而这一特性在基于语法的空间中往往缺失。这是因为程序语法的微小变化通常不会导致不同的智能体行为。我们通过学习一个呈现不同智能体行为的程序库来定义语义空间。然后，我们通过为局部搜索算法定义邻域函数来近似语义空间，在该函数中，我们用程序库中的程序替换当前候选程序的部分内容。我们在名为MicroRTS的实时策略游戏中评估了我们的假设。实证结果支持我们的假设，即在语义空间中搜索可以比在基于语法的空间中搜索更具样本效率。
英文摘要：
Syntax-guided synthesis is commonly used to generate programs encoding policies. In this approach, the set of programs, that can be written in a domain-specific language defines the search space, and an algorithm searches within this space for programs that encode strong policies. In this paper, we propose an alternative method for synthesizing programmatic policies, where we search within an approximation of the language's semantic space. We hypothesized that searching in semantic spaces is more sample-efficient compared to syntax-based spaces. Our rationale is that the search is more efficient if the algorithm evaluates different agent behaviors as it searches through the space, a feature often missing in syntax-based spaces. This is because small changes in the syntax of a program often do not result in different agent behaviors. We define semantic spaces by learning a library of programs that present different agent behaviors. Then, we approximate the semantic space by defining a neighborhood function for local search algorithms, where we replace parts of the current candidate program with programs from the library. We evaluated our hypothesis in a real-time strategy game called MicroRTS. Empirical results support our hypothesis that searching in semantic spaces can be more sample-efficient than searching in syntax-based spaces.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
