Towards a multi-agent non-player character road network: a Reinforcement Learning approach

Stela Makri[1] and Panayiotis Charalambous[1].
2021 IEEE Conference on Games (CoG).
[1] CYENS - Centre of Excellence
...

Abstract

Creating detailed and interactive game environ- ments is an area of great importance in the video game industry. This includes creating realistic Non-Player Characters which respond seamlessly to the players actions. Machine learning had great contributions to the area, overcoming scalability and robustness shortcomings of hand-scripted models. We introduce the early results of a reinforcement learning approach in building a simulation environment for heterogeneous, multi-agent non- player characters in a dynamic road network game scene.

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Citation

@INPROCEEDINGS{9619047,
 author={Makri, Stela and Charalambous, Panayiotis},
 booktitle={2021 IEEE Conference on Games (CoG)},
 title={Towards a multi-agent non-player character road network: a Reinforcement Learning approach},
 year={2021},
 volume={},
 number={},
 pages={1-5},
 doi={10.1109/CoG52621.2021.9619047}
}