%File: anonymous-submission-latex-2025.tex \documentclass[letterpaper]{article} % DO NOT CHANGE THIS \usepackage[submission]{aaai25} % DO NOT CHANGE THIS \usepackage{times} % DO NOT CHANGE THIS \usepackage{helvet} % DO NOT CHANGE THIS \usepackage{courier} % DO NOT CHANGE THIS \usepackage[hyphens]{url} % DO NOT CHANGE THIS \usepackage{graphicx} % DO NOT CHANGE THIS \urlstyle{rm} % DO NOT CHANGE THIS \def\UrlFont{\rm} % DO NOT CHANGE THIS \usepackage{natbib} % DO NOT CHANGE THIS AND DO NOT ADD ANY OPTIONS TO IT \usepackage{caption} % DO NOT CHANGE THIS AND DO NOT ADD ANY OPTIONS TO IT \frenchspacing % DO NOT CHANGE THIS \setlength{\pdfpagewidth}{8.5in} % DO NOT CHANGE THIS \setlength{\pdfpageheight}{11in} % DO NOT CHANGE THIS % % These are recommended to typeset algorithms but not required. See the subsubsection on algorithms. Remove them if you don't have algorithms in your paper. \usepackage{algorithm} \usepackage{algorithmic} \usepackage{subfigure} \usepackage{diagbox} \usepackage{booktabs} \usepackage{amsmath} \usepackage{amssymb} \usepackage{mathtools} \usepackage{amsthm} \usepackage{tikz} \usepackage{bm} \usepackage{esvect} \usepackage{multirow} \theoremstyle{plain} % \newtheorem{theorem}{Theorem}[section] \newtheorem{theorem}{Theorem} \newtheorem{proposition}[theorem]{Proposition} \newtheorem{lemma}[theorem]{Lemma} \newtheorem{corollary}[theorem]{Corollary} \theoremstyle{definition} \newtheorem{definition}[theorem]{Definition} \newtheorem{assumption}[theorem]{Assumption} \theoremstyle{remark} \newtheorem{remark}[theorem]{Remark} % % These are are recommended to typeset listings but not required. See the subsubsection on listing. 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Written by AAAI Press Staff\textsuperscript{\rm 1}\thanks{With help from the AAAI Publications Committee.}\\ AAAI Style Contributions by Pater Patel Schneider, Sunil Issar,\\ J. Scott Penberthy, George Ferguson, Hans Guesgen, Francisco Cruz\equalcontrib, Marc Pujol-Gonzalez\equalcontrib } \affiliations{ %Afiliations \textsuperscript{\rm 1}Association for the Advancement of Artificial Intelligence\\ % If you have multiple authors and multiple affiliations % use superscripts in text and roman font to identify them. % For example, % Sunil Issar\textsuperscript{\rm 2}, % J. Scott Penberthy\textsuperscript{\rm 3}, % George Ferguson\textsuperscript{\rm 4}, % Hans Guesgen\textsuperscript{\rm 5} % Note that the comma should be placed after the superscript 1101 Pennsylvania Ave, NW Suite 300\\ Washington, DC 20004 USA\\ % email address must be in roman text type, not monospace or sans serif proceedings-questions@aaai.org % % See more examples next } %Example, Single Author, ->> remove \iffalse,\fi and place them surrounding AAAI title to use it \iffalse \title{My Publication Title --- Single Author} \author { Author Name } \affiliations{ Affiliation\\ Affiliation Line 2\\ name@example.com } \fi \iffalse %Example, Multiple Authors, ->> remove \iffalse,\fi and place them surrounding AAAI title to use it \title{My Publication Title --- Multiple Authors} \author { % Authors First Author Name\textsuperscript{\rm 1}, Second Author Name\textsuperscript{\rm 2}, Third Author Name\textsuperscript{\rm 1} } \affiliations { % Affiliations \textsuperscript{\rm 1}Affiliation 1\\ \textsuperscript{\rm 2}Affiliation 2\\ firstAuthor@affiliation1.com, secondAuthor@affilation2.com, thirdAuthor@affiliation1.com } \fi % REMOVE THIS: bibentry % This is only needed to show inline citations in the guidelines document. You should not need it and can safely delete it. \usepackage{bibentry} % END REMOVE bibentry \begin{document} \setcounter{theorem}{0} \maketitle % \setcounter{theorem}{0} \begin{abstract} The existing research on value-based reinforcement learning also minimizes the error. However, is error minimization really the only option for value-based reinforcement learning? We can easily observe that the policy on action choosing probabilities is often related to the relative values, and has nothing to do with their absolute values. Based on this observation, we propose the objective of variance minimization instead of error minimization, derive many new variance minimization algorithms, both including a traditional parameter $\omega$, and conduct an analysis of the convergence rate and experiments. The experimental results show that our proposed variance minimization algorithms converge much faster. \end{abstract} % Uncomment the following to link to your code, datasets, an extended version or similar. % % \begin{links} % \link{Code}{https://aaai.org/example/code} % \link{Datasets}{https://aaai.org/example/datasets} % \link{Extended version}{https://aaai.org/example/extended-version} % \end{links} \input{main/introduction.tex} \input{main/preliminaries.tex} \input{main/motivation.tex} \input{main/theory.tex} \input{main/experiment.tex} \input{main/relatedwork.tex} \input{main/conclusion.tex} \bibliography{aaai25} \end{document}