### Background  
Reconfigurable intelligent surfaces (RISs) are a revolutionary technology in the field of wireless communication and signal propagation [12]. The structure of RISs typically includes a dielectric surface panel, which is a subgroup of periodic structures [13] composed of repeating minimal geometric shapes called unit cells. Each unit cell contains conductive printed patches, also known as scatterers, with sizes that are a small fraction of the operating frequency wavelength. The macroscopic effect of these scatterers defines a specific impedance surface [14], which, when controlled, can manipulate reflected waves from the dielectric surface panel. Each scatterer or cluster of scatterers can be adjusted to reconstruct electromagnetic waves with desired characteristics across the entire surface. By intelligently controlling the phase, magnitude, and polarization of reflected or transmitted waves, RISs can enhance wireless communication performance, improve signal coverage, and reduce interference in various wireless systems. Electric vehicles (EVs) are considered an emerging strategy to reduce the dependence on oil and provide opportunities to reduce carbon emissions [15]. The main elements of an EV system include EVs, charging stations equipped with charging points, and associated communication systems. Managing EV charging and optimizing their interaction with the power grid relies on appropriate communication infrastructure between EVs, charging stations, and the power grid. EV charging communication networks play a crucial role in promoting the widespread adoption of electric vehicles by providing reliable communication connections for EV owners, thereby contributing to the transition to sustainable transportation. In [16], social networks are utilized for searching internet resources, routing traffic, or selecting effective content distribution strategies. The Internet of Things (IoT) [17] integrates a vast array of technologies, envisioning various things or objects interacting and cooperating with each other through a series of communication protocols to achieve common goals. The convergence of the Internet of Things and social networks into the Social Internet of Things (SIoT) [18] is anticipated to have many desirable impacts on the future world. The SIoT aims to enhance the functionality, usability, and effectiveness of IoT systems by leveraging social relationships. By enabling IoT devices to collaborate, share information, and interact based on social context, the SIoT seeks to create more intelligent and adaptable IoT environments capable of addressing diverse user needs and preferences. Meanwhile, causal reinforcement learning (CRL) [19; 20] is a branch of reinforcement learning (RL) that incorporates causal reasoning into the decision-making process. Causal structures in machine learning refer to the graphical representation of causal relationships among variables in a given system. These structures capture the cause-effect relationships between different variables, enabling the identification of causal factors and the prediction of system behavior. Understanding causal structures is essential for making informed decisions, conducting causal inference, and designing effective machine learning models that can accurately capture and leverage causal relationships. In the complex communication environment considered in this paper, actions may not directly lead to observedoutcomes. Instead, they may influence outcomes through intermediate variables, allowing CRL algorithms to make wiser decisions. Furthermore, asynchronous advantage actor-critic (A3C) [21] is a type of reinforcement learning algorithm that combines the advantages of both policy-based and value-based methods. A3C uses asynchronous training to update multiple agents concurrently, allowing for more efficient exploration of the action space and faster convergence to optimal policies.