Visual Dialog Agent Based On Deep Q Learning and Memory Module Networks

Abstract

In the past many years, it has been observed that there has been an increase in methods to solve problems and the solution involves a combination of Computer Vision and Natural Language Processing. New algorithms and systems are emerging and are being developed every day to solve the above-mentioned kind of problems. Visual Dialog Agent is one of them. This kind of system utilizes both Computer Vision and Natural Language Processing algorithms. With this technology many variants of Visual Dialog Agents have been designed till date and many exclusive algorithms are created for Visual Dialog Agent. In this paper we propose an idea to create a Visual Dialog Agent which utilizes the present state of art End to End Memory Module Networks along with Reinforcement Learning Policies to answer the questions prompted by the user and as well understand the inclination of the user in the conversation which it holds. The goal of the proposed Visual Dialog Agent is to have a more engaging conversation with the highest user inclination.