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Deep Reinforcement Learning: A Brief Survey

Arulkumaran, K; Deisenroth, MP; Brundage, M; Bharath, AA; (2017) Deep Reinforcement Learning: A Brief Survey. IEEE Signal Processing Magazine , 34 (6) pp. 26-38. 10.1109/MSP.2017.2743240. Green open access

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Abstract

Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level 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.

Type: Article
Title: Deep Reinforcement Learning: A Brief Survey
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/MSP.2017.2743240
Publisher version: https://doi.org/10.1109/MSP.2017.2743240
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Artificial intelligence, Signal processing algorithms, Visualization, Machine learning, Learning (artificial intelligence), Neural networks
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10083557
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