A Model-Driven Generative Self Play-Based Toolchain for Developing Games and Players

ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE), pp. 95–107

Abstract

Turn-based games such as chess are very popular, but tool-chains tailored for their development process are still rare. In this paper we present a model-driven and generative toolchain aiming to cover the whole development process of rule-based games. In particular, we present a game description language enabling the developer to model the game in a logics-based syntax. An executable game interpreter is generated from the game model and can then act as an environment for reinforcement learning-based self-play training of players. Before the training, the deep neural network can be modeled manually by a deep learning developer or generated using a heuristics estimating the complexity of mapping the state space to the action space. Finally, we present a case study modeling three games and evaluate the language features as well as the player training capabilities of the toolchain.

Authors 4

1. Evgeny Kusmenko Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany

2. Maximilian Münker Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany

3. Matthias Nadenau Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany

4. Bernhard Rumpe⋆ Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany