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\usepackage{graphicx}
\usepackage[margin=1in]{geometry}
\usepackage{hyperref}
\usepackage{algorithm}
\usepackage{algorithmic}
\usepackage{float}
\usepackage{booktabs}
\usepackage{caption}
\usepackage{subcaption}

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\newcommand{\sectionheading}[1]{\noindent\textbf{#1}}

% Title and author information
\title{\Large{Your Project Title: A Study in Optimal Control and Reinforcement Learning}}
\author{Your Name\\
        Course Name\\
        Institution}
\date{\today}

\begin{document}

\maketitle

\begin{abstract}
Solar Racing is a competition with the goal of creating highly efficient solar-assisted electric vehicles. Effective solar racing
requires awareness and complex decision making to determine optimal speeds to exploit the environmental conditions, such as winds,
cloud cover, and changes in elevation. We present an environment modelled on the dynamics involved for a race, including generated
elevation and wind profiles. The model uses the \texttt{gymnasium} interface to allow it to be used by a variety of algorithms.
We demonstrate a method of designing reward functions for multi-objective problems. The environment shows to be solvable by modern
reinforcement learning algorithms.
\end{abstract}

\section{Introduction}
Start with a broad context of your problem area in optimal control/reinforcement learning. Then narrow down to your specific focus. Include:
\begin{itemize}
    \item Problem motivation
    \item Brief overview of existing approaches
    \item Your specific contributions
    \item Paper organization
\end{itemize}

Solar racing was invented in the early 90s as a technology incubator for high-efficiency motor vehicles. The first solar races were speed
focused, however a style of race that focused on minimal energy use within a given route was developed to push focus towards vehicle efficiency.
The goal of these races is to arrive at a destination within a given time frame, while using as little grid (non-solar) energy as possible.
Aerodynamic drag is one of the most significant sources of energy consumption, along with elevation changes. The simplest policy to meet
the constraints of on-time arrival is:
$$
V_{\text{avg}} = \frac{D}{T}
$$
Where $D$ is the distance needed to travel, and $T$ is the maximum allowed time.

\section{Background}
Provide necessary background on:
\begin{itemize}
    \item Your specific application domain
    \item Relevant algorithms and methods
    \item Previous work in this area
\end{itemize}

\section{Methodology}
Describe your approach in detail:
\begin{itemize}
    \item Problem formulation
    \item Algorithm description
    \item Implementation details
\end{itemize}

% Example of how to include an algorithm
\begin{algorithm}[H]
\caption{Your Algorithm Name}
\begin{algorithmic}[1]
\STATE Initialize parameters
\WHILE{not converged}
    \STATE Update step
\ENDWHILE
\RETURN Result
\end{algorithmic}
\end{algorithm}

\section{Experiments and Results}
Present your findings:
\begin{itemize}
    \item Experimental setup
    \item Results and analysis
    \item Comparison with baselines (if applicable)
\end{itemize}

% Example of how to include figures
\begin{figure}[H]
\centering
\caption{Description of your figure}
\label{fig:example}
\end{figure}

% Example of how to include tables
\begin{table}[H]
\centering
\caption{Your Table Caption}
\begin{tabular}{lcc}
\toprule
Method & Metric 1 & Metric 2 \\
\midrule
Approach 1 & Value & Value \\
Approach 2 & Value & Value \\
\bottomrule
\end{tabular}
\label{tab:results}
\end{table}

\section{Discussion}
Analyze your results:
\begin{itemize}
    \item Interpretation of findings
    \item Limitations and challenges
    \item Potential improvements
\end{itemize}

\section{Conclusion}
Summarize your work:
\begin{itemize}
    \item Key contributions
    \item Practical implications
    \item Future work directions
\end{itemize}

\bibliography{references}
\bibliographystyle{plain}

\end{document}
