Game Adaptation by Using Reinforcement Learning Over Meta Games

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Simão Reis (Author) Other; Luís Paulo Reis (Author) FEUP; Nuno Lau (Author) Other

Journal

Title: Group Decision and Negotiation

Vol. 30

Pages: 321-340

ISSN: 0926-2644

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-00R-MAW

DOI: 10.1007/s10726-020-09652-8

Abstract (EN): In this work, we propose a Dynamic Difficulty Adjustment methodology to achieve automatic video game balance. The balance task is modeled as a meta game, a game where actions change the rules of another base game. Based on the model of Reinforcement Learning (RL), an agent assumes the role of a game master and learns its optimal policy by playing the meta game. In this new methodology we extend traditional RL by adding the existence of a meta environment whose state transition depends on the evolution of a base environment. In addition, we propose a Multi Agent System training model for the game master agent, where it plays against multiple agent opponents, each with a distinct behavior and proficiency level while playing the base game. Our experiment is conducted on an adaptive grid-world environment in singleplayer and multiplayer scenarios. Our results are expressed in twofold: (i) the resulting decision making by the game master through gameplay, which must comply in accordance to an established balance objective by the game designer; (ii) the initial conception of a framework for automatic game balance, where the balance task design is reduced to the modulation of a reward function (balance reward), an action space (balance strategies) and the definition of a balance space state.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20