Deep reinforcement learning

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Deep reinforcement learning (DRL) uses deep learning and reinforcement learning principles to create efficient algorithms applied on areas like robotics, video games, NLP (computer science), computer vision, education, transportation, finance and healthcare.[1] Implementing deep learning architectures (deep neural networks) with reinforcement learning algorithms (Q-learning, actor critic, etc.) is capable of scaling to previously unsolvable problems.[2] That is because DRL is able to learn from raw sensors or image signals as input. A remarkable milestone in DQN is that agent uses end-to-end reinforcement learning with convolutional neural networks for playing ATARI games.[3]

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. arXiv:1811.12560. Bibcode:2018arXiv181112560F. 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. arXiv:1708.05866. Bibcode:2017ISPM...34...26A. 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.