Reinforcement Learning and Deep Reinforcement Learning

  • F. Richard Yu
  • Ying He
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)


In order to better understand state-of-the-art reinforcement learning agent, deep Q-network, a brief review of reinforcement learning and Q-learning are first described. Then recent advances of deep Q-network are presented, and double deep Q-network and dueling deep Q-network that go beyond deep Q-network are also given.


  1. 1.
    R. S. Sutton, “Introduction: The challenge of reinforcement learning,” in Reinforcement Learning. Springer, 1992, pp. 1–3.Google Scholar
  2. 2.
    H. Y. Ong, K. Chavez, and A. Hong, “Distributed deep Q-learning,” arXiv preprint arXiv:1508.04186, 2015.Google Scholar
  3. 3.
    V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015.CrossRefGoogle Scholar
  4. 4.
    Z. Wang, N. de Freitas, and M. Lanctot, “Dueling network architectures for deep reinforcement learning,” arXiv preprint arXiv:1511.06581, 2015.Google Scholar
  5. 5.
    M. Hausknecht and P. Stone, “Deep reinforcement learning in parameterized action space,” arXiv preprint arXiv:1511.04143, 2015.Google Scholar
  6. 6.
    T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971, 2015.Google Scholar
  7. 7.
    S. Gu, T. Lillicrap, I. Sutskever, and S. Levine, “Continuous deep Q-learning with model-based acceleration,” arXiv preprint arXiv:1603.00748, 2016.Google Scholar
  8. 8.
    S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” Journal of Machine Learning Research, vol. 17, no. 39, pp. 1–40, 2016.MathSciNetzbMATHGoogle Scholar
  9. 9.
    M. Deisenroth and C. E. Rasmussen, “Pilco: A model-based and data-efficient approach to policy search,” in Proceedings of the 28th International Conference on machine learning (ICML-11), 2011, pp. 465–472.Google Scholar
  10. 10.
    V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602, 2013.Google Scholar
  11. 11.
    H. Van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double q-learning,” CoRR, abs/1509.06461, 2015.Google Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  • F. Richard Yu
    • 1
  • Ying He
    • 1
  1. 1.Carleton UniversityOttawaCanada

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