In this post, I will be talking about a unique way to use reinforcement learning (RL) in deep learning applications. I definitely recommend brushing up some deep learning fundamentals and if possible, some policy gradient fundamentals as well before you get started with this post.
Traditionally, RL is used to solve sequential decision making problems in the video game space or robotics space or any other space where there is a concrete RL task at hand. There are several applications in the fields of video games and robotics where the task at hand can be very easily seen as an RL problem and can be modeled appropriately as well. But RL as a technique is quite versatile and can be used in several other domains to train neural networks that are traditionally trained in a supervised fashion. We’ll talk about one very important such application in this post. Along the way, I’ll also try to convince you that this isn’t really a different way to use RL but rather just a different way to look at the traditional RL problem. So with that, lets begin!
Developing RL techniques for IR and NLP applications
Developed siamese deep learning architectures to find similar questions from a Q&A archive on a distance learning platform
Designing a curriculum for job applicants by analysing data on job descriptions. Assignment done for the [MITACS Globalink programme](https://www.mitacs.ca/en/programs/globalink)
Search engine on a nuclear corpus and an outline of an approach to build a factoid-based question answering system
Search engine on a nuclear corpus and an outline of an approach to build a factoid-based question answering system