Reinforcement Learning Algorithms with Selector, Tuner, or Estimator

Author:

Masadeh Ala’eddinORCID,Wang Zhengdao,E. Kamal Ahmed

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference37 articles.

1. Arulkumaran, K.; Deisenroth, M.P.; Brundage, M.; Bharath, A.A.: Deep reinforcement learning: a brief survey. IEEE Signal Process. Mag. 34(6), 26–38 (2017)

2. Sutton, R.S.; Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, USA (2018)

3. Ho, M.A.T., Yamada, Y., Umetani, Y.: An hmm-based temporal difference learning with model-updating capability for visual tracking of human communicational behaviors. In: Proceeding of the IEEE international conference on automatic face and gesture recognition, pp. 170–175 (Washington, DC, USA, May 2002)

4. Hu, J.; Zhao, F.; Meng, J.; Wu, S.: Application of deep reinforcement learning in the board game. Big Data Artif. Intell. (ICIBA) 1, 809–812 (2020)

5. Jagodnik, K.M.; Thomas, P.S.; van den Bogert, A.J.; Branicky, M.S.; Kirsch, R.F.: Training an actor-critic reinforcement learning controller for arm movement using human-generated rewards. IEEE Trans. Neural Syst. Rehabilit. Eng. 25(10), 1892–1905 (2017)

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