Signal Processing - November 2017 - 26

DEEP LEARNING FOR VISUAL UNDERSTANDING

Kai Arulkumaran, Marc Peter Deisenroth,
Miles Brundage, and Anil Anthony Bharath

Deep Reinforcement Learning
A brief survey

D

eep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents
a step toward building autonomous systems with a higherlevel understanding of the visual world. Currently, deep learning is enabling reinforcement learning (RL) to scale to problems
that were previously intractable, such as learning to play video
games directly from pixels. DRL 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 RL, then
progress to the main streams of value-based and policy-based
methods. Our survey will cover central algorithms in deep RL,
including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic. In
parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via RL. To conclude,
we describe several current areas of research within the field.

Introduction

©ISTOCKPHOTO.COM/ZAPP2PHOTO

Digital Object Identifier 10.1109/MSP.2017.2743240
Date of publication: 13 November 2017

26

One of the primary goals of the field of AI is to produce fully
autonomous agents that interact with their environments to learn
optimal behaviors, improving over time through trial and error.
Crafting AI systems that are responsive and can effectively
learn has been a long-standing challenge, ranging from robots,
which can sense and react to the world around them, to purely
software-based agents, which can interact with natural language and multimedia. A principled mathematical framework
for experience-driven autonomous learning is RL [78]. Although
RL had some successes in the past [31], [53], [74], [81], previous
approaches lacked scalability and were inherently limited to fairly
low-dimensional problems. These limitations exist because RL
algorithms share the same complexity issues as other algorithms:
memory complexity, computational complexity, and, in the case
of machine-learning algorithms, sample complexity [76]. What
we have witnessed in recent years-the rise of deep learning,
relying on the powerful function approximation and representation learning properties of deep neural networks-has provided
us with new tools to overcoming these problems.

IEEE SIGNAL PROCESSING MAGAZINE

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November 2017

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1053-5888/17©2017IEEE



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Table of Contents for the Digital Edition of Signal Processing - November 2017

Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
Signal Processing - November 2017 - 1
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Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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