A Mutual Influence-based Learning Algorithm

Stefan Rudolph, Sven Tomforde, Jörg Hähner


Robust and optimized agent behavior can be achieved by allowing for learning mechanisms within the underlying adaptive control strategies. Therefore, a classic feedback loop concept is used that chooses the best action for an observed situation – and learns the success by analyzing the achieved performance. This typically reflects only the local scope of an agent and neglects the existence of other agents with impact on the reward calculation. However, there are significant mutual influences among agents population. For instance, the success of a Smart Camera’s control strategy depends (in terms of person detection or 3D-reconstruction) largely on the current strategy performed by its spatially neighbors. In this paper, we compare two concepts to consider such influences within the adaptive control strategy: Distributed W-Learning and Q-Learning in combination with mutual influence detection. We demonstrate that the performance can be improved significantly, if taking detected influences into account.


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Paper Citation

in Harvard Style

Rudolph S., Tomforde S. and Hähner J. (2016). A Mutual Influence-based Learning Algorithm . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-172-4, pages 181-189. DOI: 10.5220/0005697001810189

in Bibtex Style

author={Stefan Rudolph and Sven Tomforde and Jörg Hähner},
title={A Mutual Influence-based Learning Algorithm},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},

in EndNote Style

JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A Mutual Influence-based Learning Algorithm
SN - 978-989-758-172-4
AU - Rudolph S.
AU - Tomforde S.
AU - Hähner J.
PY - 2016
SP - 181
EP - 189
DO - 10.5220/0005697001810189