Reinforcement Learning for Information Retrieval
Type: Full-day (Sunday, March 28)
About the tutorial
Reinforcement Learning (RL) enables agents to learn optimal decision making through interaction within a dynamic environment. Recent advances in deep learning and RL have allowed intelligent agents to exhibit unprecedented success in various domains and achieve super-human performance in many tasks. RL and deep learning are impacting almost all areas of academia and industry today, and there is a growing interest in applying them to Information Retrieval (IR). Companies like Google and Alibaba have already started to use RL-based search and recommendation engines to personalize their services and enhance user experiences on their ecosystem.
Current online resources for learning RL either focus on theory at the expense of hands-on practice or are limited to implementations without sufficient intuition and theoretical background. This full-day tutorial has been carefully tailored for IR researchers and practitioners to gain both theoretical knowledge and hands-on experience on the most popular RL methods using PyTorch and Python Jupyter notebooks on Google Colab. We aim to equip the participants with a working knowledge of RL which will help them better understand the latest IR publications involving RL and enable them to tackle their own IR problems using RL.
Our tutorial does not require any previous knowledge on the topic and starts with the fundamental concepts and algorithms such as Markov Decision Process, Exploration vs Exploitation, Q-Learning, Policy Gradient and Actor-Critic algorithms. We focus particularly on the combination of Reinforcement Learning and Deep Learning, with algorithms such as Deep Q-Network (DQN). Lastly, we describe how these techniques can be utilized to address representative IR problems like “learning to rank” and discuss recent developments as well as an outlook for future research.