loading
Papers

Research.Publish.Connect.

Paper

Authors: Stefan J. L. Knegt 1 ; Madalina M. Drugan 2 and Marco A. Wiering 1

Affiliations: 1 University of Groningen, Netherlands ; 2 ITLearns.Online, Netherlands

ISBN: 978-989-758-275-2

Keyword(s): Reinforcement Learning, Opponent Modelling, Q-learning, Computer Games.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: In this paper we propose the use of vision grids as state representation to learn to play the game Tron using neural networks and reinforcement learning. This approach speeds up learning by significantly reducing the number of unique states. Furthermore, we introduce a novel opponent modelling technique, which is used to predict the opponent’s next move. The learned model of the opponent is subsequently used in Monte-Carlo roll-outs, in which the game is simulated n-steps ahead in order to determine the expected value of conducting a certain action. Finally, we compare the performance using two different activation functions in the multi-layer perceptron, namely the sigmoid and exponential linear unit (Elu). The results show that the Elu activation function outperforms the sigmoid activation function in most cases. Furthermore, vision grids significantly increase learning speed and in most cases this also increases the agent’s performance compared to when the full grid is used as stat e representation. Finally, the opponent modelling technique allows the agent to learn a predictive model of the opponent’s actions, which in combination with Monte-Carlo roll-outs significantly increases the agent’s performance. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.215.182.36

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Knegt, S.; M. Drugan, M. and A. Wiering, M. (2018). Opponent Modelling in the Game of Tron using Reinforcement Learning.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-275-2, pages 29-40. DOI: 10.5220/0006536300290040

@conference{icaart18,
author={Stefan J. L. Knegt. and Madalina M. Drugan. and Marco A. Wiering.},
title={Opponent Modelling in the Game of Tron using Reinforcement Learning},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2018},
pages={29-40},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006536300290040},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Opponent Modelling in the Game of Tron using Reinforcement Learning
SN - 978-989-758-275-2
AU - Knegt, S.
AU - M. Drugan, M.
AU - A. Wiering, M.
PY - 2018
SP - 29
EP - 40
DO - 10.5220/0006536300290040

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.