Adaptive Control & Reinforcement Learning 

Course contents: Dynamic programming, Policy Evaluation, Policy Improvement, Policy Iteration, Maximum Principle for optimal control, the Hamilton-Jacobi-Bellman equation, the linear quadratic regulator, neural networks, system identification, learning dynamics, conditions of persistence of excitation, approximate dynamic programming, excitation-based online approximate optimal control, Lyapunov-based stability theory. Examples and applications in the areas of robotics, cyber-physical systems, autonomous vehicles and transportation systems. Students will develop: (a) A solid understanding of the principles behind adaptive control and neuro-adaptive reinforcement learning in continuous-time, discrete-time, continuous spaces and discrete spaces. (b) A solid understanding of the most common reinforcement learning algorithms, as well as their theoretical and practical limitations. Students will be endowed with a fundamental background that will allow them to pursue systems-centered PhD paths or explore future opportunities in the automation, optimization, and control systems marketplace.