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\title{Efficient Monte Carlo Tree Search via On-the-Fly \\ State-Conditioned Action Abstraction}

% The standard author block has changed for UAI 2024 to provide
% more space for long author lists and allow for complex affiliations
%
% All author information is authomatically removed by the class for the
% anonymous submission version of your paper, so you can already add your
% information below.
%
% Add authors
\author[1]{Yunhyeok~Kwak\thanks{Equal contribution.}}
\author[1]{Inwoo~Hwang\footnote[1]}
\author[1]{Dooyoung~Kim}
\author[1,2]{Sanghack~Lee\thanks{Corresponding authors.}}
\author[1]{Byoung-Tak~Zhang\footnote[2]{}}
\affil[1]{
AI Institute, Seoul National University
}
\affil[2]{
Graduate School of Data Science, Seoul National University
}
  
\begin{document}
\maketitle

\begin{abstract}
\input{sections/00-abs}
\end{abstract}


\section{Introduction}
\label{sec:intro}
\input{sections/01-intro}

\section{Preliminaries}
\label{sec:preliminaries}
\input{sections/02-preliminaries}

\section{Method}
\label{sec:method}
\input{sections/03-method}

\section{Experiments}
\label{sec:experiments}
\input{sections/04-experiment}

\section{Related Work}
\label{sec:related_work}
\input{sections/05-related_work}

\section{Conclusion}
\label{sec:conclusion}
\input{sections/06-conclusion}



\begin{acknowledgements}
We would like to thank anonymous reviewers for constructive feedback.
This work was partly supported by the IITP (RS-2021-II212068-AIHub/10\%, RS-2021-II211343-GSAI/10\%, 2022-0-00951-LBA/10\%, 2022-0-00953-PICA/20\%), NRF (RS-2023-00211904/20\%, RS-2023-00274280/10\%, RS-2024-00353991/10\%), and KEIT (RS-2024-00423940/10\%) grant funded by the Korean government.
\end{acknowledgements}

% References
\bibliography{reference}

\newpage

\onecolumn

\title{Efficient Monte Carlo Tree Search via On-the-Fly\\
State-Conditioned Action Abstraction\\(Supplementary Material)}
\maketitle

\appendix


% \section{Supplementary Material}
\label{appendix}
\input{sections/90-appendix}

\end{document}
