ACTION VALUE FUNCTION APPROXIMATION BASED ON RADIAL BASIS FUNCTION NETWORK FOR REINFORCEMENT LEARNING

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DERHAMI VALI; MEHRABI OMID

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Journal: Journal of Control Year:2011 | Volume:5 | Issue:1 Page(s): 50-63

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Title

ACTION VALUE FUNCTION APPROXIMATION BASED ON RADIAL BASIS FUNCTION NETWORK FOR REINFORCEMENT LEARNING

Author(s)

DERHAMI VALI | MEHRABI OMID | Issue Writer Certificate

Keywords

NEURAL REINFORCEMENT LEARNINGQ3

CRITIC-ONLY ARCHITECTUREQ3

RBF NEURAL NETWORKQ2

SARSAQ3

STATIONARY POINTSQ3

Abstract

One of the challenges encountered in the application of classical reinforcement learning methods to real-control problems is the curse of dimensiality. In order to overcome this difficulty, hybrid algorithms that combine reinforcement learning with various function approximators have attracted many research interests. In this paper, a novel NEURAL REINFORCEMENT LEARNING (NRL) scheme which is based on SARSA learning and Radial Basis Function (RBF) network is proposed. The RBF network is used to approximate the Action Value Function (AVF) on-line. The inputs of RBF network are state-action pairs of system and its outputs are corresponding approximated AVF. As the necessary condition for the convergence ofNSL to the optimal task performance, the existence of STATIONARY POINTS for NSL which coincide with the fixed points of Approximate Action Value Iteration (AAVI) are proved. The validity of the proposed algorithmis tested through simulation examples: mountain car control task, and acrobot problem. Overall results demonstrate that our algorithm can effectively improve convergence speed and the efficiency of experience exploitation.

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APA: Copy

DERHAMI, VALI, & MEHRABI, OMID. (2011). ACTION VALUE FUNCTION APPROXIMATION BASED ON RADIAL BASIS FUNCTION NETWORK FOR REINFORCEMENT LEARNING. JOURNAL OF CONTROL, 5(1), 50-63. SID. https://sid.ir/paper/120813/en

Vancouver: Copy

DERHAMI VALI, MEHRABI OMID. ACTION VALUE FUNCTION APPROXIMATION BASED ON RADIAL BASIS FUNCTION NETWORK FOR REINFORCEMENT LEARNING. JOURNAL OF CONTROL[Internet]. 2011;5(1):50-63. Available from: https://sid.ir/paper/120813/en

IEEE: Copy

VALI DERHAMI, and OMID MEHRABI, “ACTION VALUE FUNCTION APPROXIMATION BASED ON RADIAL BASIS FUNCTION NETWORK FOR REINFORCEMENT LEARNING,” JOURNAL OF CONTROL, vol. 5, no. 1, pp. 50–63, 2011, [Online]. Available: https://sid.ir/paper/120813/en

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