%0 Generic %1 arulkumaran2017brief %A Arulkumaran, Kai %A Deisenroth, Marc Peter %A Brundage, Miles %A Bharath, Anil Anthony %D 2017 %K ai rl survey %R 10.1109/MSP.2017.2743240 %T A Brief Survey of Deep Reinforcement Learning %U http://arxiv.org/abs/1708.05866 %X 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.

@misc{arulkumaran2017brief, abstract = {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.}, added-at = {2021-02-10T14:13:16.000+0100}, author = {Arulkumaran, Kai and Deisenroth, Marc Peter and Brundage, Miles and Bharath, Anil Anthony}, biburl = {https://www.bibsonomy.org/bibtex/22ad5cec7163654175ef30dbbeac7085b/louissf}, description = {A Brief Survey of Deep Reinforcement Learning}, doi = {10.1109/MSP.2017.2743240}, interhash = {c6d11756420138700f58faf7057f7142}, intrahash = {2ad5cec7163654175ef30dbbeac7085b}, keywords = {ai rl survey}, note = {cite arxiv:1708.05866Comment: IEEE Signal Processing Magazine, Special Issue on Deep Learning for Image Understanding (arXiv extended version)}, timestamp = {2021-02-10T14:13:16.000+0100}, title = {A Brief Survey of Deep Reinforcement Learning}, url = {http://arxiv.org/abs/1708.05866}, year = 2017 }