# An Overview of Reinforcement Learning Methods for Variable Speed Limit Control
## 1 Introduction
It is also worthy to mention other applications of RL in sustainable mobility, like RL-based RM [24], traffic signal control [25], vehicle relocation and ride request assignment in shared autonomous mobility-on-demand systems [26] or RL-based assisted power management [27] for electric hybrid bicycle in bike-sharing systems [28]. However, the efficiency of the combined RL and VSL (RL-VSL) approach is highly related to the learning process. The learning process is performed in traffic simulations since it applies random speed limit values in the beginning. Thus, potentially creating dangerous situations for a real-world implementation. Simulations cover scenarios depending on the initial parameters with a drawback of not covering all possible relevant traffic states (incidents, driver behavior, the influence of different weather situations). The learning process should be performed in a structured manner [29], complementing existing traffic scenarios with synthesized ones that evoke or replicate substantial aspects of all relevant real traffic scenarios [30]. Although the desirable learning convergence can be achieved by using a smaller number of traffic scenarios, it can be infeasible to apply RL-VSL in real-world motorway environments due to the stochastic and continuous nature of traffic flows. Thus, choosing the appropriate learning process is crucial and there is a need for a systematic review of applying RL for VSL. To the best of our knowledge, there is no comprehensive systematic overview of applications of RL algorithms in the domain of VSL. An overview of VSL control strategies, including classical VSL strategies and theoretical background of influences of VSL on traffic flow and its applications, is presented in [5]. However, it lacks the part related to RL based approaches. So far, the main motivation for this research is to present the state of the art of currently used RL algorithms for solving the VSL optimization problems on urban motorways. This research provides a clear, comprehensive overview of the concept of RL with applications in the field of VSL, pointing out open questions. Therefore, the main contributions of this paper are:  
* In this study, the systematic literature review approach is applied. A keyword-based search was used, and 12 primary studies were systematically identified from search results. * Traffic management studies on motorways focused on RL approaches to solve the VSL optimization problem are covered by this study. * Unlike existing studies such as [31], which focus on summarizing the approaches of VSLs traffic management systems, this study tries to assess how well the present RL-VSL approaches work based on the provided results. First, the objectives of the approaches, such as improving efficiency or safety, were identified and categorized. Then, different approaches were compared on how well they meet a specific goal. In addition, the RL methods used to solve VSL and how the VSL problem is being modeled for a particular objective for intelligent traffic management on motorways were identified and summarized.