Ghanbari, A., Vaghei, Y., Sayyed Noorani, S. (2014). Reinforcement Learning in Neural Networks: A Survey. International Journal of Advanced Biological and Biomedical Research, 2(5), 1398-1416.

Ahmad Ghanbari; Yasaman Vaghei; Sayyed Mohammad Reza Sayyed Noorani. "Reinforcement Learning in Neural Networks: A Survey". International Journal of Advanced Biological and Biomedical Research, 2, 5, 2014, 1398-1416.

Ghanbari, A., Vaghei, Y., Sayyed Noorani, S. (2014). 'Reinforcement Learning in Neural Networks: A Survey', International Journal of Advanced Biological and Biomedical Research, 2(5), pp. 1398-1416.

Ghanbari, A., Vaghei, Y., Sayyed Noorani, S. Reinforcement Learning in Neural Networks: A Survey. International Journal of Advanced Biological and Biomedical Research, 2014; 2(5): 1398-1416.

Reinforcement Learning in Neural Networks: A Survey

^{1}Faculty of Mechanical Engineering, and Mechatronics Research Laboratory, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran

^{2}Mechatronics Research Laboratory, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran

Receive Date: 31 July 2014,
Accept Date: 31 July 2014

Abstract

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applications. Although many surveys investigated general RL, no survey is specifically dedicated to the combination of artificial neural networks and RL. This paper therefore describes the state of the art of NNRL algorithms, with a focus on robotics applications. In this paper, a comprehensive survey is started with a discussion on the concepts of RL. Then, a review of several different NNRL algorithms is presented. Afterwards, the performances of different NNRL algorithms are evaluated and compared in learning prediction and learning control tasks from an empirical aspect and the paper concludes with a discussion on open issues.

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