The paper focuses on the problem of learning saccades enabling visual object search. The developed system combines reinforcement learning with a neural network for learning to predict the possible outcomes of its actions. We validated the solution in three types of environment consisting of (pseudo)-randomly generated matrices of digits. The experimental verification is followed by the discussion regarding elements required by systems mimicking the fovea movement and possible further research directions.
@article{arxiv.1610.06492,
title = {Utilization of Deep Reinforcement Learning for saccadic-based object visual search},
author = {Tomasz Kornuta and Kamil Rocki},
journal= {arXiv preprint arXiv:1610.06492},
year = {2016}
}
Comments
Paper submitted to special session on Machine Intelligence organized during 23rd International AUTOMATION Conference