Continuous Procedural Network of Roads Generation using L-Systems and Reinforcement Learning

Ciprian Paduraru, Miruna Paduraru, Stefan Iordache

2022

Abstract

Procedural content generation methods are nowadays used in areas such as games, simulations or the movie industry to generate large amounts of data with lower development costs. Our work attempts to fill a gap in this area by focusing on methods capable of generating content representing network of roads, taking into account real-world patterns or user-defined input structures. At the low- level of our generative processes, we use L-systems and Reinforcement Learning based solutions that are employed to generate tiles of road structures in environments that are partitioned as 2D grids. As the evaluation section shows, these methods are suitable for runtime demanding applications since the computational cost is not significant.

Paper Citation

in Harvard Style

Paduraru C., Paduraru M. and Iordache S. (2022). Continuous Procedural Network of Roads Generation using L-Systems and Reinforcement Learning. In Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-588-3, pages 425-432. DOI: 10.5220/0011268300003266

in Bibtex Style

@conference{icsoft22,
author={Ciprian Paduraru and Miruna Paduraru and Stefan Iordache},
title={Continuous Procedural Network of Roads Generation using L-Systems and Reinforcement Learning},
booktitle={Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2022},
pages={425-432},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011268300003266},
isbn={978-989-758-588-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Continuous Procedural Network of Roads Generation using L-Systems and Reinforcement Learning
SN - 978-989-758-588-3
AU - Paduraru C.
AU - Paduraru M.
AU - Iordache S.
PY - 2022
SP - 425
EP - 432
DO - 10.5220/0011268300003266