Searching for Programmatic Policies in Semantic Spaces

TL;DR

This paper introduces a semantic space search method for programmatic policies, which is more sample-efficient than traditional syntax-based search, demonstrated through experiments in a real-time strategy game.

Contribution

The paper proposes a novel semantic space search approach for synthesizing policies, leveraging learned program libraries and neighborhood functions for improved efficiency.

Findings

Semantic space search is more sample-efficient than syntax-based search.

The method effectively synthesizes policies in a real-time strategy game.

Empirical results support the efficiency advantage of semantic search.

Abstract

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.