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

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