Application of SONQL for real-time learning of robot behaviors

作者：Carreras Marc^*; Yuh Junku; Baffle Joan; Ridao Pere

来源：Robotics and Autonomous Systems, 2007, 55(8): 628-642.

DOI：10.1016/j.robot.2007.03.003

• reinforcement learning
• Q-learning
• behavior-based robotics
• autonomous vehicles

This paper describes the Semi-Online Neural-Q-leaming (SONQL) algorithm designed for real-time learning of reactive robot behaviors. The Q-function is generalized by a multilayer neural network allowing the use of continuous states. The algorithm uses a database of the most recent learning samples to accelerate and improve the convergence. Each SONQL algorithm represents an independent, reactive and adaptive state-action mapping, which implements the function of a robot behavior for one degree of freedom (DOF). The generalization capability of the SONQL algorithm was demonstrated by computer simulation with the '' mountain-car '' benchmark. The SONQL was also investigated by experiment on a mobile robot for a target-following task. Experimental results show that the SONQL is promising for online robot learning.

• 出版日期2007-8-31
• 单位国家自然科学基金委员会