Q-Learning with a Growing RBF Network for Behavior Learning in Mobile Robotics

J. Li and T. Duckett (Sweden)


Qlearning, Resource allocating network, Robot behaviour


The use of artificial neural networks for approximating value functions in reinforcement learning is a common practice, but usually requires much work on designing the network architecture and refining of the network parame ters. In this paper we present a simple learning system that uses Q-learning with a resource allocating network (RAN) for behaviour learning in mobile robotics. The resource allocating network is used as a function approximator to dynamically represent the continuous sensory space, thus acquiring the sensorimotor mapping for generalization; and Q-learning is used to learn the control policy in ‘off-policy’ fashion that enables the human operator to guide the initial learning process, thus speeding up the reinforcement learn ing. We illustrate our approach using a PeopleBot robot to acquire a wall-following behaviour, and discuss some ob servations on the convergence and online training of our learning algorithm in the experiments.

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