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\begin{document}

\title{Acyclicity of Game 2048}

\author{Xingguo Chen,  Xinwen Li, Shangdong Yang, and Wenhao Wang
\thanks{Manuscript received XXXX; revised XXXX; accepted XXXX.
Date of publication XXXX; date of current version XXXX. The work was supported
by the National Natural Science Foundation, China (Nos. 62276142, 62206133, and 62202240).}
\IEEEcompsocitemizethanks{
\IEEEcompsocthanksitem X. Chen,  X. Li and S. Yang are
 with  the Jiangsu Key Laboratory of Big Data Security \&
Intelligent Processing, Nanjing University of Posts and Telecommunications,
 P.R., China \protect (e-mail:
chenxg@njupt.edu.cn).}%
\IEEEcompsocitemizethanks{
\IEEEcompsocthanksitem W. Wang is 
 with College of Electronic Engineering, National University of Defense Technology, 
 P.R., China \protect (e-mail:
wangwenhao11@nudt.edu.cn).
\emph{(Corresponding author: W. Wang.)}%
}

}

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\markboth{IEEE Transaction on Games,~Vol.~14, No.~8, August~202X}%
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\begin{abstract}
In  reinforcement learning  of 2048 game, 
we are intrigued by the absence of successful cases 
involving explicit exploration, e.g., $\epsilon-$greedy, 
softmax. 
Through experiments comparing 2048 game and maze, 
we argue that explicit exploration strategies
 cannot be effectively combined to learn in the 2048 game,
  and demonstrate the acyclic nature of the 2048 game. 
  The successful experiences in 2048 game AI 
  will contribute to solving acyclic MDPs.
\end{abstract}

\begin{IEEEkeywords}
Acyclicity, 2048 game, ergodicity, backward learning.
\end{IEEEkeywords}


\input{main/introduction}
\input{main/background}
\input{main/acyclic}
\input{main/2048isAcyclic}
\input{main/discussion}


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