## 1 Introduction  
Optical beam wavefronts can experience distortion as they propagate through atmospheric turbulence, as illustrated in Figure 1. This distortion can lead to a reduction in the potential bandwidth of the link [1, 2]. Currently, most optical satellite communication ground stations direct optical beams onto photodetectors or couple them into multi-mode fiber to improve coupling efficiency [3]. However, this approach comes with limitations, as it restricts the use of highly efficient phase modulation schemes as well as the use of fiber amplifiers for long-haul data transmission. Coupling into single-mode fiber can enable >10 Gbps data rates per wavelength channel, but it requires much higher optical powers from the satellite to compensation for reduced coupling efficiencies. Larger telescope apertures provide little benefit since they are more affected by atmospheric turbulence, leading to diminishing returns on the signal. The issue of atmospheric turbulence has been successfully mitigated in astronomy using adaptive optics (AO) [4, 5, 6, 7]. The traditional AO system dynamically corrects the wavefronts using a deformable mirror through closed loop feedback from a wavefront sensor. Traditional AO systems are still costly and complex, with a significant portion of the cost arising from the wavefront sensor, especially when dealing with infrared beams in optical satellite-to-ground links which exhibit higher read noise and require cooling unlike silicon cameras [8]. Furthermore, they consume a significant fraction of the incident beam intensity to adequately illuminate the camera pixels and add latency to the system. This latency can lead to outdated wavefront measurements in fast moving low earth orbit (LEO) satellites, resulting in errors at the space and time scales of optical coherence [9]. Recently, research has demonstrated the potential of reinforcement learning (RL) to tackle complex control problems in various domains of AO [10], such as astronomy [11; 12; 13], wavefront sensor-based systems [14; 15; 16; 17], wavefront sensorless AO systems [18; 19; 20], and microscope systems that, unlike atmospheric turbulence, deal with lower-speed turbulence caused by aqueous solutions and optical aberrations in a microscope applications [21]. The existing implementations of RL on AO systems are not optimized for optical satellite communication due to their primary focus on optimizing image sharpness rather than improving the reliability of optical data links. In other words, although image sharpness can be used for fiber-coupling efficiency, it gathers excessive information that can compromise loop speed. Ensuring a stable and dependable optical link is prioritized in moving towards a consistent and uninterrupted high-data transmission with lower latency and cost. A low pixel count detector mitigates high-latency and expensive read-out circuits, such as those found in infrared cameras. Additionally, image-sharpness-based implementations are specifically customized for optimizing wavefront distortion under the long coherence time conditions found in environments like microscopy and ophthalmology. In RL, agents' decisions are based on the information observed from the environment. However, the observations received by an agent might not contain all the necessary information for decision-making, leading to a partially observable environment. Control methods for partially observable dynamic environments are an outstanding challenge [22]. Examples of applications that fall under these conditions are free-space communication [23], laser machining, astronomy [24], retinal imaging, microscopy, real-time video upscaling, underwater imaging and communication, and even real-time music mixing/production, where lower observability can lead to reduced latencies and cost in time-sensitive applications. This research aims to realize fast, reliable, and lower-cost satellite communications downlinks, one of the most challenging of these applications.