Navigation of Autonomous Vehicles in Unknown Environments using Reinforcement Learning

In this paper we propose a generic approach for navigation of nonholonomic vehicles in unknown environments. The vehicle model is also unknown, so the path planner uses reinforcement learning to acquire the optimal behaviour together with the model, which is estimated by a reduced set of transitions. After the training phase, the vehicle is able to explore the environment through a wall-following behaviour. In order to guide the navigation and to build a map of the environment the planner employs virtual walls. The learning time to acquire a good approximation of the wall-following behaviour was only a few minutes. Both simulation and experimental results are reported to show the satisfactory performance of the method.