Application of Deep Reinforcement Learning to Control Problems

Author:

Ikemoto Junya1,Ushio Toshimitsu1

Affiliation:

1. Graduate School of Engineering Science, Osaka University

Publisher

Japanese Neural Network Society

Reference18 articles.

1. 1) Hinton, S., Osindero, S., Whye, Y. (2006): A fast learning algorithm for deep belief nets, Neural Computation, Vol.18, No.7, pp.1527-1544.

2. 2) Goodfellow, I., Bengio, Y., Courville, A. (2016): Deep Learning, MIT Press.

3. 4) Ichikawa, I., Ushio, T. (2019): Application of reinforcement learning to adaptive control of connected vehicle, nonlinear theory and its applications, IEICE, Vol.10, No.4, pp.443-454.

4. 6) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller. M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D. (2015): Human-level control through deep reinforcement learning, Nature, Vol.518, pp.529-533.

5. 7) Masuda, N., Ushio, T. (2017): Control of nonholonomic vehicle system using hierarchical deep reinforcement learning, in Proc. NOLTA 2017, pp.26-29.

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