The field of spacecraft guidance and control is moving towards the development of more advanced and autonomous systems. Researchers are exploring the use of reinforcement learning and digital twinning to improve the performance and robustness of guidance and control systems. Notably, reinforcement learning is being used to train guidance and control networks, allowing for more adaptability and flexibility in stochastic conditions. Digital twinning is also being used to validate and verify the performance of guidance and control systems, enabling more realistic and accurate testing. Overall, these advancements are enabling the development of more sophisticated and reliable spacecraft systems. Noteworthy papers include: Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networks, which introduces a novel RL training framework and demonstrates the superiority of RL-trained G&CNETs in stochastic conditions. Digital and Robotic Twinning for Validation of Proximity Operations and Formation Flying, which presents a unified digital and robotic twinning framework for validating multi-modal GNC systems.