@inproceedings{05d46e77f8dd4737808c4ff22dc64d04,
title = "Quantized measurements in Q-learning based model-free optimal control",
abstract = "Quantization noise is present in many real-time applications due to the resolution of analog-to-digital conversions. This can lead to error in policies that are learned by model-free Q-learning. A method for quantization error reduction for Q-learning algorithms is developed using the sample time and an exploration noise that is added to the control input. The method is illustrated with discrete-time policy and value iteration algorithms using both a simulated environment and a real-time physical system.",
author = "Sini Tiistola and Risto Ritala and Matti Vilkko",
note = "JUFOID=86671; IFAC World Congress ; Conference date: 11-07-2020 Through 17-07-2020",
year = "2020",
doi = "10.1016/j.ifacol.2020.12.2219",
language = "English",
series = "IFAC-PapersOnLine",
publisher = "Elsevier",
number = "2",
pages = "1640--1645",
editor = "Rolf Findeisen and Sandra Hirche and { Janschek}, Klaus and Martin M{\"o}nnigmann",
booktitle = "21th IFAC World Congress",
address = "Netherlands",
}