Simulated Autonomous Driving on Realistic Road Networks using Deep Reinforcement Learning
share
Using Deep Reinforcement Learning (DRL) can be a promising approach to handle tasks in the field of (simulated) autonomous driving, whereby recent publications only consider learning in unusual driving environments. This paper outlines a developed software, which instead can be used for evaluating DRL algorithms based on realistic road networks and therefore in more usual driving environments. Furthermore, we identify difficulties when DRL algorithms are applied to tasks, in which it is not only important to reach a goal, but also how this goal is reached. We conclude this paper by presenting the results of an application of a new DRL algorithm, which can partly solve these problems.
READ FULL TEXT