Python Agent in Ludii

TL;DR

This paper introduces Python interfaces for Ludii, a Java-based general game system, enabling Python agent development and comparing the performance of different Java-Python integration libraries.

Contribution

It provides and evaluates Python interfaces for Ludii using jpy and Py4J, analyzing their performance for general game playing agents.

Findings

jpy is faster than Py4J for Ludii agent integration

Performance varies with game complexity and algorithm used

Regression models predict jpy's superior speed over Py4J

Abstract

Ludii is a Java general game system with a considerable number of board games, with an API for developing new agents and a game description language to create new games. To improve versatility and ease development, we provide Python interfaces for agent programming. This allows the use of Python modules to implement general game playing agents. As a means of enabling Python for creating Ludii agents, the interfaces are implemented using different Java libraries: jpy and Py4J. The main goal of this work is to determine which version is faster. To do so, we conducted a performance analysis of two different GGP algorithms, Minimax adapted to GGP and MCTS. The analysis was performed across several combinatorial games with varying depth, branching factor, and ply time. For reproducibility, we provide tutorials and repositories. Our analysis includes predictive models using regression, which suggest that jpy is faster than Py4J, however slower than a native Java Ludii agent, as expected.