REINFORCEMENT LEARNING IN NEURAL NETWORKS: A SURVEY

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GHANBARI AHMAD; VAGHEI YASAMAN; SAYYED NOORANI SAYYED MOHAMMAD REZA

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Paper Information

Journal: INTERNATIONAL JOURNAL OF ADVANCED BIOLOGICAL AND BIOMEDICAL RESEARCH (IJABBR) Year:2014 | Volume:2 | Issue:5 Page(s): 1398-1415

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Title

REINFORCEMENT LEARNING IN NEURAL NETWORKS: A SURVEY

Author(s)

GHANBARI AHMAD | VAGHEI YASAMAN | SAYYED NOORANI SAYYED MOHAMMAD REZA | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKS

REINFORCEMENT LEARNING

ADAPTIVE NETWORKS

SURVEY

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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APA: Copy

GHANBARI, AHMAD, VAGHEI, YASAMAN, & SAYYED NOORANI, SAYYED MOHAMMAD REZA. (2014). REINFORCEMENT LEARNING IN NEURAL NETWORKS: A SURVEY. INTERNATIONAL JOURNAL OF ADVANCED BIOLOGICAL AND BIOMEDICAL RESEARCH (IJABBR), 2(5), 1398-1415. SID. https://sid.ir/paper/338951/en

Vancouver: Copy

GHANBARI AHMAD, VAGHEI YASAMAN, SAYYED NOORANI SAYYED MOHAMMAD REZA. REINFORCEMENT LEARNING IN NEURAL NETWORKS: A SURVEY. INTERNATIONAL JOURNAL OF ADVANCED BIOLOGICAL AND BIOMEDICAL RESEARCH (IJABBR)[Internet]. 2014;2(5):1398-1415. Available from: https://sid.ir/paper/338951/en

IEEE: Copy

AHMAD GHANBARI, YASAMAN VAGHEI, and SAYYED MOHAMMAD REZA SAYYED NOORANI, “REINFORCEMENT LEARNING IN NEURAL NETWORKS: A SURVEY,” INTERNATIONAL JOURNAL OF ADVANCED BIOLOGICAL AND BIOMEDICAL RESEARCH (IJABBR), vol. 2, no. 5, pp. 1398–1415, 2014, [Online]. Available: https://sid.ir/paper/338951/en

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