Using Deep Reinforcement Learning to Tackle Complex Problems
What's it about?
Recently, Deep Reinforcement Learning (Deep-RL) was used to beat the best human in the world at the traditional Korean board-game Go, as well as learn to beat every other chess engine (and consequently human) in the world within four hours. This tech talk aims to explain the core factors that separate Reinforcement Learning (RL) from the other main fields of Artificial Intelligence and outline how it can be used to tackle extremely complex problems that most people assume are currently untouchable. Alex will focus on the intuition behind Deep-RL, how it compares (and differs) to other machine learning methods, as well as discuss some potential commercial applications.
Whos it for?
Anyone who is interested in learning more about Artificial Intelligence, Machine Learning and how it can be applied to different industries.
Alex Long is a D2D CRC Computer Science PhD Student at the University of New South Wales (UNSW) supervised by Alan Blair. His primary research topic is Deep Reinforcement Learning, specifically the application of RL methods to the Natural Language Processing domain. Prior to UNSW, Alex studied an MSc in Electrical Engineering at the Institute for Cognitive Systems, within the Technische Universität München (TUM) where he published work on artificial skin for humanoid robotics.
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