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% The file aaai25.sty is the style file for AAAI Press
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\title{A Variance Minimization Approach to Temporal-Difference Learning}
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    Written by AAAI Press Staff\textsuperscript{\rm 1}\thanks{With help from the AAAI Publications Committee.}\\
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    Sunil Issar,\\
    J. Scott Penberthy,
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\begin{document}
\setcounter{theorem}{0}
\maketitle
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\begin{abstract}
    % In this paper, we introduce the concept of improving the performance of parametric 
    % Temporal-Difference (TD) learning algorithms by the Variance Minimization (VM) parameter, $\omega$, 
    % which is dynamically updated at each time step. Specifically, we incorporate the VM parameter into off-policy linear algorithms such as TDC and ETD, resulting in the 
    % Variance Minimization TDC (VMTDC) algorithm and the Variance Minimization ETD (VMETD) algorithm. In the two-state counterexample, 
    % we analyze 
    % the convergence speed of these algorithms by calculating the minimum eigenvalue of the key 
    % matrices and find that the VMTDC algorithm converges faster than TDC, while VMETD is more stable in convergence than ETD
    %  through the 
    % experiment.In controlled experiments, the VM algorithms demonstrate 
    % superior performance.
    Under certain conditions, the larger the smallest 
    eigenvalue of the key matrix of an algorithm, the 
    faster the algorithm converges. By observation, most 
    current objective functions aim to minimize error. 
    Therefore, in this paper, we propose two new objective 
    functions and derive three Variance Minimization (VM) algorithms, including VMTD, VMTDC and VMETD.
    A scalar parameter, $\omega$, is introduced, to improve the performance of parametric 
    Temporal-Difference (TD) learning algorithms.
    In the policy evaluation experiment, two-state, 
    we analyze 
    the convergence speed of these algorithms by calculating the minimum eigenvalue of the key 
    matrices both on-policy and off-policy.In controlled experiments, the VM algorithms demonstrate 
    superior performance.


\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}
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\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}
