Approximation Bayesian Reinforcement Learning based on Estimation of Plant Variation and its Application to Peg-in-Hole Task

Author:

Senda Kei1,Hishinuma Toru1,Tani Yurika1

Affiliation:

1. Graduate School of Engineering, Kyoto University

Publisher

The Institute of Systems, Control and Information Engineers

Reference8 articles.

1. [1] R. S. Sutton and A. G. Barto: Reinforcement Learning, MIT Press (1998)

2. [2] D. P. Bertsekas and J. N. Tsitsiklis: Neuro-Dynamic Programming, Athena Scientfic (1996)

3. [3] J. Kober, D. Bagnell and J. Peters: Reinforcement learning in robotics: A survey; Int. J. Robotics Research, Vol. 32, No. 11, pp. 1238-1274 (2013)

4. [4] R. S. Sutton, et al.: Policy gradient methods for reinforcement learning with function approximation; Proc. NIPS 12, pp. 1057-1063 (1999)

5. [6] M. Strens: A Bayesian framework for reinforcement learning; Proc. ICML, pp. 943-950 (2000)

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