\chapter{Future Work}
\section{Evaluation}
We have mentioned the inadequacies of our model evaluation.
In future work, we will need to implement an evaluation method that can evaluate different methods for the crawling scheduler problem fairly.
Currently, we propose to use the generated crawling plans as the final results for comparison.
We will need to define a set of metrics for evaluating the crawling plan results: the freshness of the capture, accuracy of the model, and resource consumption.

\section{Further Baseline Model Design}
The current baselines we have included in the project can only make predictions for web content changes.
In the future, we need to design the model to generate the actual crawling plans.
What is more, we have only covered the simple models in our literature review.
We should implement or reproduce some more recent works such as \cite{serverChange,radinsky_predicting_2013}.

\section{Further RL Model Design}
For further RL model design, we will work on the following improvements:
\begin{itemize}
    \item Tune the model for optimal performance regarding the web page crawling scheduling problem in this project
    \item Apply the model for web site structure change to see if transfer learning is possible
    \item Design an RL model that can combine the web page crawling plan and web site crawling plan
    \item Explore more features that might benefit the model on top of the web content changing frequencies
\end{itemize}
