Safe Learning and Control

This lecture by the instructor covers topics related to safe learning and control in intelligent systems, focusing on autonomy under uncertainty, safe trajectory planning, stochastic modeling, multi-agent coordination, and convergence to Nash equilibrium. The presentation delves into the challenges of incorporating constraints in model-free learning, formulating safe learning problems, and seeking equilibria with black-box information. The proposed approaches to safe black-box learning, randomization in learning, and multi-agent learning in transportation systems are also discussed. The lecture concludes with insights on control systems' current state and future directions, emphasizing the importance of system safety and interdisciplinary collaboration.