### Our Contribution  
Compared to a previous work [31], this paper differs mainly in the following aspects: Focus and Background:  
This paper focuses on dynamic resource allocation in RIS-assisted electric vehicle charging communication networks under cellular networks, with base stations as the core, especially addressing the wireless communication environment within electric vehicle charging networks. In contrast, the second summary emphasizes dynamic resource allocation in RIS-assisted mobile ad hoc networks (MANETs), particularly in addressing time-varying and uncertain wireless communication environments within multi-mobile ad hoc wireless networks. Optimization Methods:  
This paper proposes an asynchronous advantage actor-critic (A3C) algorithm based on causal factors to optimize communication network resource allocation control. It learns feature representations from incomplete communication environment states to accelerate the training speed, understand the causal relationships in the environment, and transfer training results to similar communication environments. On the other hand, the second summary introduces an inner-outer joint online optimization algorithm for RIS-assisted MANETs. It utilizes the D-UCB algorithm for RISs and spectrum selection in the outer network and employs the TD3 algorithm to gain decentralized insights into RIS phase shifts and power allocation strategies in the inner network. Algorithmic Structure:  
The CF-A3C algorithm in this paper first acquires causal factors, uses them as state acquaintances of the A3C network, and updates the global network using experiences collected by multiple worker threads, eliminating the need for a replay buffer and promoting efficient exploration in resource allocation tasks. Conversely, the TD3 algorithm in the second summary adopts an actor-critic structure with three target networks and two hidden layer streams in each neural network to segregate state-value and action-value distribution functions, accelerating convergence speed and enhancing the learning efficiency. This paper presents a novel framework aimed at addressing the challenges associated with integrating the Social Internet of Things (SIoT) with connected electric vehicle (EV) charging networks. This framework harnesses emerging technologies, including reconfigurable intelligent surfaces (RISs), causal structures, and A3C-based reinforcement learning techniques, to optimize resource allocation and enhance SIoT support within EV charging networks. By integrating RIS technology, which enables control over electromagnetic waves, and applying causal RL algorithms, the framework dynamically adjusts resource allocation strategies to accommodate the evolving conditions in distributed EV charging networks. Importantly, this framework aims to simultaneously meet real-world social requirements, such as fulfilling EV user charging needs, while ensuring efficient utilization of network resources, thereby enhancing communication performance. The primary achievements elucidated within this manuscript are as follows:  
* Establishment of a model to represent the fluctuating and uncertain wireless communication setting for managing dynamic resource allocation in RIS-assisted electric vehicle charging communication networks. The model depicts the dynamic resource allocation system operating within an electric vehicle charging network. * Design of a causal inference model capable of reasoning about and addressing causal relationships in the electric vehicle charging communication network by acquiring effective representation distributions. * Proposal of a causal-factor-based asynchronous advantage actor-critic (A3C) algorithm based on the designed causal factor model for optimizing communication network resource allocation control. The feature representations are derived from learning the incomplete communication environment states.