## 1 Introduction  
Rotary steering technology, as an emerging drilling innovation, has gained increasing attention from scholars. This marks a substantial stride towards intelligent and automated drilling, particularly in challenging environments [1; 2]. Rotary steering technology offers numerous advantages, encompassing rapid drilling velocities, reduced accident frequencies, and excellent maneuverability. Furthermore, it results in cost savings. This technological advancement delineates the trajectory of progress in drilling methodologies and procedures, offering the potential for extended horizontal displacements, diminished risks of borehole obstructions, and the obviation of the need for recurrent insertion and retrieval from the borehole, thereby augmenting drilling efficiency [3; 4].  
The core of a rotary steering system (RSS) comprises a stabilized platform situated within the drill collar, which is integral to governing the manipulation of the tool face angle, as the precise control of the tool face angle is paramount for achieving directional drilling and managing wellbore inclination [5; 6; 7; 8]. Researchers have proposed various methods for controlling the attitude of stabilized platforms in rotary steering drilling, including PID control [9], fuzzy control with sliding mode variable structure control [10], and output feedback linearization control [11]. However, classic approaches have significant limitations; since control performance is closely tied to control parameters, some approaches do not even account for frictional nonlinearity effects. Moreover, the stabilized platform always exists with unknown disturbances and parameter perturbation, and frictional nonlinearity is a severe disturbance. Most conventional control methods are unable to address all situations at the design stage. It is interesting to note that previous studies have not extensively researched the impact of LuGre friction, which further accentuates the need for innovative control strategies that can overcome these challenges.  
Deep Deterministic Policy Gradient (DDPG) is a reinforcement learning algorithm that can be used to solve continuous motion control problems, including nonlinear control challenges [12]. In comparison to traditional control methodologies, DDPG stands out for its adaptability and robustness [13]. Furthermore, DDPG demonstrates excellent generalization capabilities and scalability, rendering it suitable for a wide array of control scenarios and system dynamic models [14; 15]. Therefore, in this study, the DDPG algorithm is applied to the control system of a rotary steering drilling stabilized platform; building upon this foundation, we introduce a novel approach, the Disturbance-Observer-Based Deep Deterministic Policy Gradient, to effectively counteract the impact of non-linear frictional disturbances. The specific tasks are outlined as follows:  
1. A rotary steering drilling stabilized platform model is established, and a LuGre friction model is constructed to provide a basis for the attitude control strategy.
2. A DDPG-based deep reinforcement learning attitude control system for the stabilized platform is developed. This involves the selection of the state vector, the design of the reward function, and the construction of the Actor-Critic network structure.
3. A Disturbance-Observer-Based Deep Deterministic Policy Gradient is proposed, which is aimed at effectively suppressing frictional disturbances and enhancing the control performance and robustness of the system.