Deep reinforcement learning

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Deep reinforcement learning (DRL) uses deep learning and reinforcement learning principles in order to create efficient algorithms that can be applied on areas like robotics, video games, finance, healthcare.[1] Implementing deep learning architecture (deep neural networks or etc) with reinforcement learning algorithm (Q-learning, actor critic or etc), a powerful model (DRL) can be created that is capable to scale to problems that were previously unsolvable.[2] That is because DRL usually uses raw sensor or image signals as input as can be seen in DQN for ATARI games[3], and can receive the benefit of end-to-end reinforcement learning as well as that of convolutional neural network.

References[edit]

  1. ^ Francois-Lavet, Vincent; Henderson, Peter; Islam, Riashat; Bellemare, Marc G.; Pineau, Joelle (2018). "An Introduction to Deep Reinforcement Learning". Foundations and Trends in Machine Learning. 11 (3–4): 219–354. doi:10.1561/2200000071. ISSN 1935-8237.
  2. ^ Arulkumaran, K.; Deisenroth, M. P.; Brundage, M.; Bharath, A. A. (November 2017). "Deep Reinforcement Learning: A Brief Survey". IEEE Signal Processing Magazine. 34 (6): 26–38. doi:10.1109/MSP.2017.2743240. ISSN 1053-5888.
  3. ^ Mnih, Volodymyr; et al. (December 2013). Playing Atari with Deep Reinforcement Learning (PDF). NIPS Deep Learning Workshop 2013.