A Brief Survey of Deep Reinforcement Learning

 This is the start of a new season and we all could get some help from reviews such as this one. If you know of other, please ping me. Kai mentioned this review he co-wrote on his twitter feed:

A Brief Survey of Deep Reinforcement Learning w/ @mpd37 & @Miles_Brundage: https://t.co/pNBYQ8kv7h

— Kaixhin (@KaiLashArul) 22 août 2017

A Brief Survey of Deep Reinforcement Learning by Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep Q-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.  

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