High-performance Algorithms using Deep Learning in Turn-based Strategy Games

Tomihiro Kimura, Ikeda Kokolo

Abstract

The development of AlphaGo has increased the interest of researchers in applying deep learning and reinforcement learning to games. However, using the AlphaZero algorithm on games with complex data structures and vast search space, such as turn-based strategy games, has some technical challenges. The problem involves performing complex data representations with neural networks, which results in a very long learning time. This study discusses methods that can accelerate the learning of neural networks by solving the problem of the data representation of neural networks using a search tree. The proposed algorithm performs better than existing methods such as the Monte Carlo Tree Search (MCTS). The automatic generation of learning data by self-play does not require a big learning database beforehand. Moreover, the algorithm also shows excellent match results with a win rate of more than 85% against the conventional algorithms in the new map which is not used for learning.

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Paper Citation


in Harvard Style

Kimura T. and Kokolo I. (2020). High-performance Algorithms using Deep Learning in Turn-based Strategy Games.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 555-562. DOI: 10.5220/0008956105550562


in Bibtex Style

@conference{icaart20,
author={Tomihiro Kimura and Ikeda Kokolo},
title={High-performance Algorithms using Deep Learning in Turn-based Strategy Games},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={555-562},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008956105550562},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - High-performance Algorithms using Deep Learning in Turn-based Strategy Games
SN - 978-989-758-395-7
AU - Kimura T.
AU - Kokolo I.
PY - 2020
SP - 555
EP - 562
DO - 10.5220/0008956105550562