Recent Advancements in Deep Reinforcement Learning

Abstract

With the successful triumph of deep neural networks in computer vision and the subsequent breakthroughs in the field of deep Q-learning by Mnih et al. beginning in 2013, the field of deep reinforcement learning came more and more into focus and achieved remarkable results in robotics, gaming, health care, and many other areas of research. This paper reviews recent advances of large influence in the field of deep reinforcement learning and introduces the necessary foundations for understanding those advancements.

Date
Mo, 8 May, 2020 13:30 — 14:00
Location
Remote
Henrik Hain
Henrik Hain
Data Scientist / Data Engineer

My (research) interests evolve around the practical and theoretical aspects of software engineering, (self-) learning systems and algorithms, especially (deep) reinforcement learning, spatio-temporal event detection, and computer vision approaches.

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