## 3 Conventional Channel Estimation and Machine Learning-Based Solutions  
Classical optimization algorithms face significant challenges in meeting the demands of emerging applications in RIS-assisted integrated satellite (RIS-IS) operations and NTNs. These challenges primarily arise from the complexity of communication scenarios, unknown channel models, and difficulties in accurately modeling dynamic environments through mathematical expressions, which is further exacerbated by non-linearities due to hardware losses. The continuously changing configuration of RISs further complicates matters, hindering traditional methods' applicability for real-time operations such as Internet of Things (IoT) or Internet of Vehicles (IoV) environments. However, deep reinforcement learning (DRL), a combination of DL and reinforcement learning (RL), shows its effectiveness in handling these challenges. DRL's ability to operate with minimal labeled training data makes it adaptable for optimizing wireless communication systems. This is especially true for complex and dynamic channel environments. Without total channel model information, DRL constructs wireless channel knowledge by interacting with the environment and receiving rewards. When combined with efficiently designed algorithms, sequentially finding optimal solutions to multi-objective optimization problems becomes a reality when this neural network is combined with efficiently designed algorithms. This approach excels in joint optimization-based networks, such as beamforming design, power allocation strategy, interference coordination, and user distribution model tracking in diverse communication scenarios, showcasing its usefulness in addressing the delicate challenges of RIS-assisted NTNs. With its ability to navigate complex and dynamic channel environments using minimal labeled training data, DRL showcases its superiority over standard schemes. The comparison emphasizes DRL's efficiency in real-time decision-making for joint optimization tasks, such as transmit beamforming and RIS phase shift matrix optimization. The adaptability and effectiveness of DRL in addressing the intricate challenges of RIS-assisted NTNs stand out from traditional methods, suggesting that it represents a more advanced and efficient approach to channel estimation in this context. A direct examination of the reciprocity in channel estimation for both uplink and downlink communication with RISs reveals fascinating dimensions rooted in actual scientific understanding [98]. While fully acknowledging the established reciprocity in time division duplexing, introducing RISs as something that breaks the reciprocity introduces genuine intrigue. Research on AI and ML algorithms could be pivotal in mapping channel responses and optimizing RIS behavior based on incident angles and frequency responses, presenting a solid approach to tailoring specific deployment scenarios [98; 99]. This fusion of AI, ML, and RIS optimization gives the potential for advancements in wireless communication systems, particularly when considering the actual science behind optimizing phase shifts and channel state information (CSI) in NTN environments [98; 99; 100]. Integrating AI and ML to realize RISs for communication in HAPs and NTNs presents realistic possibilities based on ongoing research [8]. The article emphasizes the practical application of DL to navigate challenges in modeling the propagation environment. Reducing the dimensions of channel estimation and discerning deployment-specific characteristics can enhance the accuracy of the learning algorithms in local propagation conditions and direct optimal signal reflection on expansive surfaces. This aligns with the complex realities of user locations and the intricate effects of mutual coupling that prove to be cumbersome for traditional modeling techniques [84]. Moreover, integrating AI and ML introduces a functional solution to configure large-scale RIS deployments.