Inproceedings
1.
Sam G. Robinson; Chloe M. Barnes; Peter R. Lewis
Centralised and Decentralised Control of Video Game Agents Inproceedings
In: Advances in Computational Intelligence Systems. UKCI 2021, pp. 108–120, Springer, 2022.
Abstract | Links | BibTeX | Tags: ANNs, Game Playing, Network Comparisons, Neuroevolution
@inproceedings{Robinson2021CentralisedAgents,
title = {Centralised and Decentralised Control of Video Game Agents},
author = {Sam G. Robinson and Chloe M. Barnes and Peter R. Lewis},
doi = {10.1007/978-3-030-87094-2_10},
year = {2022},
date = {2022-09-08},
urldate = {2022-01-01},
booktitle = {Advances in Computational Intelligence Systems. UKCI 2021},
volume = {1409},
pages = {108--120},
publisher = {Springer},
series = {Advances in Intelligent Systems and Computing},
abstract = {In this paper, the game of partially observable Ms. Pacman is used as a sandbox to evaluate Artificial Neural Networks (ANNs) that control multiple opponents (i.e. the ghosts). Comparisons between one central ANN that controls all ghosts, and multiple distinct ANNs, each controlling one ghost, are made. The NEAT algorithm is employed to evolve the ANNs. We find that chasing Ms. Pacman and exploring the map are both harder behaviours to learn for a centralised controller than for decentralised control. Further, both centralised and decentralised approaches produce vastly different behaviours for exploring the map. Novel techniques for comparing networks are also explored.},
keywords = {ANNs, Game Playing, Network Comparisons, Neuroevolution},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, the game of partially observable Ms. Pacman is used as a sandbox to evaluate Artificial Neural Networks (ANNs) that control multiple opponents (i.e. the ghosts). Comparisons between one central ANN that controls all ghosts, and multiple distinct ANNs, each controlling one ghost, are made. The NEAT algorithm is employed to evolve the ANNs. We find that chasing Ms. Pacman and exploring the map are both harder behaviours to learn for a centralised controller than for decentralised control. Further, both centralised and decentralised approaches produce vastly different behaviours for exploring the map. Novel techniques for comparing networks are also explored.
