Wide and Deep Reinforcement Learning for Grid-based Action Games

Juan Montoya, Christian Borgelt

Abstract

For the last decade Deep Reinforcement Learning has undergone exponential development; however, less has been done to integrate linear methods into it. Our Wide and Deep Reinforcement Learning framework provides a tool that combines linear and non-linear methods into one. For practical implementations, our framework can help integrate expert knowledge while improving the performance of existing Deep Reinforcement Learning algorithms. Our research aims to generate a simple practical framework to extend such algorithms. To test this framework we develop an extension of the popular Deep Q-Networks algorithm, which we name Wide Deep Q-Networks. We analyze its performance compared to Deep Q-Networks and Linear Agents, as well as human players. We apply our new algorithm to Berkley’s Pac-Man environment. Our algorithm considerably outperforms Deep Q-Networks’ both in terms of learning speed and ultimate performance showing its potential for boosting existing algorithms.

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Paper Citation


in Harvard Style

Montoya J. and Borgelt C. (2019). Wide and Deep Reinforcement Learning for Grid-based Action Games.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 50-59. DOI: 10.5220/0007313200500059


in Bibtex Style

@conference{icaart19,
author={Juan Montoya and Christian Borgelt},
title={Wide and Deep Reinforcement Learning for Grid-based Action Games},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={50-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007313200500059},
isbn={978-989-758-350-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Wide and Deep Reinforcement Learning for Grid-based Action Games
SN - 978-989-758-350-6
AU - Montoya J.
AU - Borgelt C.
PY - 2019
SP - 50
EP - 59
DO - 10.5220/0007313200500059