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\begin{abstract}
Web crawling is one of the fundamental activities for many kinds of web technology organizations and companies such as Internet Archive and Google.
While companies like Google often focus on content delivery for users, web archiving organizations such as the Internet Archive pay more attention to the accurate preservation of the web. 
Crawling accuracy and efficiency are major concerns in this task.
An ideal crawling module should be able to keep up with the changes in the target web site with minimal crawling frequency to maximize the routine crawling efficiency.
In this project, we investigate using information from web archives' history to help the crawling process within the scope of news websites.
We aim to build a smart crawling module that can predict web content change accurately both on the web page and web site structure level through modern machine learning algorithms and deep learning architectures.

At the end of the project: We have collected and processed raw web archive collections from Archive.org and through our frequent crawling jobs.
We have developed methods to extract identical copies of web page content and web site structure from the web archive data.
We have implemented baseline models for predicting web page content change and web site structure change with supervised machine learning algorithms. 
We have implemented two different reinforcement learning models for generating a web page crawling plan: a continuous prediction model and a sparse prediction model. 
Our results show that the reinforcement learning modal has the potential to work as an intelligent web crawling scheduler.

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