## 1 Introduction  
In many countries, electric vehicles (EVs) have emerged as a vital strategic industry to promote energy conservation and emission reduction [1; 2; 3]. To enable the widespread adoption of electric vehicles and alleviate range anxiety (i.e., concerns about insufficient energy storage for a trip), the provision of accessible charging infrastructure is crucial [4; 5; 3]. While the number of charging stations has increased, it remains insufficient to meet the growing charging demands. Optimizing the location and deployment of charging stations, known as the charging station location problem (CSLP) [6], is a critical approach to address this challenge. The CSLP is an application of the facility location problem, aiming to select suitable locations from a candidate set to optimize various objectives. Numerous studies have focused on the CSLP as a long-standing problem [7].  
However, the optimal placement of charging stations in road networks is not without its challenges. Influenced by a myriad of factors, including road network topology, existing charging infrastructure, traffic patterns, and charging duration, determining appropriate placements is complex [6]. Additionally, existing methods often neglect practical constraints, such as the need for fairness in charging station distribution and the requirement for automated decision making in identifying viable new charging station locations. Traditional regional optimization methods, which predominantly consider isolated junctions or parking lots, are insufficient in the face of widespread charging demands across the road network [7].  
Moreover, prevalent greedy algorithms fail to account for the intricate spatiotemporal relationships between the road network and charging demand [8].  
In our research, we address these challenges head-on. Our approach emphasizes coverage and fairness in the objective function for determining charging station locations, while also considering the benefits of station placement and driver comfort. We assess the benefits of charging station placement by considering the coverage of charging stations and network nodes, while driver comfort is quantified by the total time spent by drivers during the charging process. Fairness is achieved by balancing the average coverage of charging stations across the network. We reformulate the CSLP as a reinforcement learning problem, introducing a novel algorithm, PPO-Attention, that extends the Proximal Policy Optimization algorithm [9] by integrating a policy network with a multi-head attention layer.  
In summary, our contributions are as follows:  
* We propose an optimal model for the placement of charging infrastructure that balances coverage and fairness of charging station locations while considering driver comfort.
* We formulate the CS placement problem as a reinforcement learning problem and introduce a novel reinforcement learning model, PPO-Attention. This model enhances the policy network of the Proximal Policy Optimization algorithm by incorporating an attention layer with two attention heads.
* We collect data from multiple cities and regions worldwide and evaluate the performance of our algorithm using these datasets. The results demonstrate the effectiveness and efficiency of our method, surpassing existing baseline algorithms.