Deep Reinforcement Learning for General Game Playing

Authors

  • Adrian Goldwaser University of New South Wales
  • Michael Thielscher University of New South Wales

DOI:

https://doi.org/10.1609/aaai.v34i02.5533

Abstract

General Game Playing agents are required to play games they have never seen before simply by looking at a formal description of the rules of the game at runtime. Previous successful agents have been based on search with generic heuristics, with almost no work done into using machine learning. Recent advances in deep reinforcement learning have shown it to be successful in some two-player zero-sum board games such as Chess and Go. This work applies deep reinforcement learning to General Game Playing, extending the AlphaZero algorithm and finds that it can provide competitive results.

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Published

2020-04-03

How to Cite

Goldwaser, A., & Thielscher, M. (2020). Deep Reinforcement Learning for General Game Playing. Proceedings of the AAAI Conference on Artificial Intelligence, 34(02), 1701-1708. https://doi.org/10.1609/aaai.v34i02.5533

Issue

Section

AAAI Technical Track: Game Playing and Interactive Entertainment