The field of autonomous navigation and control is witnessing significant developments, with a focus on improving the efficiency and robustness of reinforcement learning methods in complex environments. Recent studies have highlighted the importance of algorithm selection, implementation details, and fine-tuning for discovering truly smart autonomous navigation strategies. Notably, advancements in multi-agent reinforcement learning have enabled the scaling of techniques to a fleet of autonomous vehicles, allowing for efficient tracking of multiple targets in dynamic environments. Furthermore, innovative control frameworks have been proposed for robot-assisted drone recovery on wavy surfaces, leveraging techniques such as error-state Kalman filters and receding horizon model predictive control. These developments are paving the way for enhanced autonomy in maritime robotics and other applications. Noteworthy papers include: Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles, which proposes an iterative distillation method for efficient training of multi-agent policies. Robot-Assisted Drone Recovery on a Wavy Surface Using Error-State Kalman Filter and Receding Horizon Model Predictive Control, which presents a unified framework for accurate prediction and motion planning in drone recovery tasks.