Remove duplicate entry in lists

HEPML.tex

@@ -110,7 +110,7 @@
 				\\\textit{For supervised learning, the labels $y_i$ are known.  In the case that the labels are noisy or only known with some uncertainty, then the learning is called weak supervision.  Semi-supervised learning is the related case where labels are known for only a fraction of the training examples.}
 				\item \textbf{Unsupervised}~\cite{Mackey:2015hwa,Komiske:2019fks,1797846,Dillon:2019cqt,Cai:2020vzx,Howard:2021pos,Dillon:2021gag}
 				\\\textit{When no labels are provided, the learning is called unsupervised.}
-				\item \textbf{Reinforcement Learning}~\cite{Carrazza:2019efs,Brehmer:2020brs,John:2020sak,Harvey:2021oue,Cranmer:2021gdt,Windisch:2021mem,Dersy:2022bym,Nishimura:2023wdu,Nishimura:2023wdu}
+				\item \textbf{Reinforcement Learning}~\cite{Carrazza:2019efs,Brehmer:2020brs,John:2020sak,Harvey:2021oue,Cranmer:2021gdt,Windisch:2021mem,Dersy:2022bym,Nishimura:2023wdu}
 				\\\textit{Instead of learning to distinguish different types of examples, the goal of reinforcement learning is to learn a strategy (policy).  The prototypical example of reinforcement learning in learning a strategy to play video games using some kind of score as a feedback during the learning.}
 				\item \textbf{Quantum Machine Learning}~\cite{Mott:2017xdb,Zlokapa:2019lvv,Blance:2020nhl,Terashi:2020wfi,Chen:2020zkj,Wu:2020cye,Guan:2020bdl,Chen:2021ouz,Blance:2021gcs,Heredge:2021vww,Wu:2021xsj,Belis:2021zqi,Araz:2021ifk,Bravo-Prieto:2021ehz,Kim:2021wrr,Ngairangbam:2021yma,Gianelle:2022unu,Abel:2022lqr,Araz:2022haf,Delgado:2022aty,Alvi:2022fkk,Peixoto:2022zzk,Araz:2022zxk,Rousselot:2023pcj}
 				\\\textit{Quantum computers are based on unitary operations applied to quantum states.  These states live in a vast Hilbert space which may have a usefully large information capacity for machine learning.}

README.md

@@ -607,7 +607,6 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 * [A machine learning pipeline for autonomous numerical analytic continuation of Dyson-Schwinger equations](https://arxiv.org/abs/2112.13011)
 * [Simplifying Polylogarithms with Machine Learning](https://arxiv.org/abs/2206.04115)
 * [Exploring the flavor structure of quarks and leptons with reinforcement learning](https://arxiv.org/abs/2304.14176)
-* [Exploring the flavor structure of quarks and leptons with reinforcement learning](https://arxiv.org/abs/2304.14176)
 
 ####  Quantum Machine Learning
 

docs/index.md

@@ -668,7 +668,6 @@ const expandElements = shouldExpand => {
     * [A machine learning pipeline for autonomous numerical analytic continuation of Dyson-Schwinger equations](https://arxiv.org/abs/2112.13011)
     * [Simplifying Polylogarithms with Machine Learning](https://arxiv.org/abs/2206.04115)
     * [Exploring the flavor structure of quarks and leptons with reinforcement learning](https://arxiv.org/abs/2304.14176)
-    * [Exploring the flavor structure of quarks and leptons with reinforcement learning](https://arxiv.org/abs/2304.14176)
 
     ####  Quantum Machine Learning