Adaptive State Space Quantisation For Reinforcement Learning Of collision-free navigation

Abstract

The paper describes a self-learning control system for a mobile robot. Based on sensor information the control system has to provide a steering signal in such a way that collisions are avoided. Since in our case no `examples' are available, the system learns on the basis of an external reinforcement signal which is negative in case of a collision and zero otherwise. Rules from Temporal Difference learning are used to find the correct mapping between the (discrete) sensor input space and the steering signal. We describe the algorithm for learning the correct mapping from the input (state) vector to the output (steering) signal, and the algorithm which is used for a discrete coding of the input state space. keywords: reinforcement learning, neural networks, state-space quantisation, mobile robot navigation. I. Introduction Control modules which map information from external sensors into motor signals are often used with mobile robots to accomplish a reflex-like collision avoidance beha...