Path-Tracking Control of a Non-Holonomic Car-Like Robot with Reinforcement Learning

Abstract

The problem investigated in this paper is that of driving a car-like robot along a race track and the use of reinforcement learning to find a good control function. The reinforcement learner uses a case-based function approximator to extend the reinfocement learning paradigm to handle continuous states. The learned controller performs similar to the best control functions in both simulation and also in practical driving.