Reinforcement learning for route choice in an abstract traffic scenario

Traffic movement in a commuting scenario is a phenomena that results from individual and uncoordinated route choice by drivers. Every driver wishes to achieve reasonable travel times from his origin to his destination and, from a global point of view, it is desirable that the load gets distributed proportionally to the roads capacity on the network. This work presents a reinforcement learning algorithm for route choice which relies solely on drivers experience to guide their decisions. Experimental results demonstrate that reasonable travel times can be achieved and vehicles distribute themselves over the road network avoiding congestion. The proposed algorithm makes use of no coordinated learning mechanism, making this work a case of use of independent learners concept.