Optimization, Control and Reinforcement Learning

Optimization serves as the backbone of modern computational strategies, offering essential solutions to complex problems. When combined with the theoretical insights of control theory and the data-driven techniques of reinforcement learning, these fields have sparked and will continue to drive innovation across a range of disciplines. This convergence opens up exciting new possibilities for interdisciplinary research, potentially giving rise to entirely new fields and research directions. This session will explore the synergies between these domains, providing a deeper understanding of their theoretical and practical applications.