The field of autonomous systems and optimal control is witnessing significant developments, with a focus on improving the performance and adaptability of control laws. Researchers are exploring innovative approaches to design and optimize performance indices, which is crucial for achieving desired behaviors and trade-offs in complex systems. Notably, the integration of reinforcement learning and model-based reinforcement learning is gaining traction, enabling the development of more adaptive and interpretable motion planning algorithms. Furthermore, advancements in terrain-aware path planning are facilitating more reliable autonomy in unstructured environments. Noteworthy papers include: Performance Index Shaping for Closed-loop Optimal Control, which proposes a novel framework for analytically linking performance indices to closed-loop optimal control laws. Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving, which demonstrates the effectiveness of using reinforcement learning to dynamically switch cost function parameters in autonomous racing. Context-Aware Model-Based Reinforcement Learning for Autonomous Racing, which introduces a context-aware extension of model-based reinforcement learning algorithms for improved generalization capabilities in autonomous driving.