Fixed Point Theory Analysis of a Lambda Policy Iteration with Randomization for the Ćirić Contraction Operator

Fuente: arXiv
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Main Authors: Belhenniche, Abdelkader, Chertovskih, Roman
Format: Preprint
Published: 2024
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author Belhenniche, Abdelkader
Chertovskih, Roman
author_facet Belhenniche, Abdelkader
Chertovskih, Roman
contents We apply methods of the fixed point theory to a Lambda policy iteration with a randomization algorithm for weak contractions mappings. This type of mappings covers a broader range than the strong contractions typically considered in the literature, such as Ćirić contraction. Specifically, we explore the characteristics of reinforcement learning procedures developed for feedback control within the context of fixed point theory. Under relatively general assumptions, we identify the sufficient conditions for convergence with a probability of one in infinite-dimensional policy spaces.
format Preprint
id arxiv_https___arxiv_org_abs_2405_07824
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Fixed Point Theory Analysis of a Lambda Policy Iteration with Randomization for the Ćirić Contraction Operator
Belhenniche, Abdelkader
Chertovskih, Roman
Optimization and Control
Functional Analysis
We apply methods of the fixed point theory to a Lambda policy iteration with a randomization algorithm for weak contractions mappings. This type of mappings covers a broader range than the strong contractions typically considered in the literature, such as Ćirić contraction. Specifically, we explore the characteristics of reinforcement learning procedures developed for feedback control within the context of fixed point theory. Under relatively general assumptions, we identify the sufficient conditions for convergence with a probability of one in infinite-dimensional policy spaces.
title Fixed Point Theory Analysis of a Lambda Policy Iteration with Randomization for the Ćirić Contraction Operator
topic Optimization and Control
Functional Analysis
url https://arxiv.org/abs/2405.07824