Abstract—In recent years, researches on adaptive control
have focused on bio-inspired learning techniques to deal with
real-life applications. Reinforcement Learning (RL) is one of
these major techniques, which has been widely used in robot
control approaches. The implementation of artificial neural
networks in RL algorithms enables more efficient optimal
control strategies. This article proposes a field application of
neural network reinforcement learning (NNRL) for walking
control of an active simulated 3-link biped robot. The adaptive
control agent consists of two neural network units, known as
actor and critic for learning prediction and learning control
tasks. Results of the presented control method reveal its
efficiency in stable walking control of the biped robot model as
a nonlinear complex dynamic task.
Index Terms—Adaptive control, biped robot, neural
network reinforcement learning,stable walking.
The authors are with Faculty of Mechanical Engineering and
Mechatronics Engineering Department, University of Tabriz, Iran (e-mail:
a-ghanbari@tabrizu.ac.ir, y.vaghei91@tabrizu.ac.ir,
smrs.noorani@tabrizu.ac.ir).
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Cite: Ahmad Ghanbari, Yasaman Vaghei, and Sayyed Mohammad Reza Sayyed Noorani, "Neural Network Reinforcement Learning for Walking
Control of a 3-Link Biped Robot," International Journal of Engineering and Technology vol. 7, no. 6, pp. 449-452, 2015.
