B5679 - OPTIMAL CONTROL AND REINFORCEMENT LEARNING M

Conoscenze e abilità da conseguire

The course focuses on theoretical and numerical methods for the design of trajectories and feedback policies of dynamical systems to optimize a performance index and satisfy given constraints. The presented methods involve the areas of optimal control, reinforcement learning and model predictive control with a strong focus on numerical optimization. At the end of the course students will know how to (i) model optimal control and reinforcement learning problems and characterize optimality conditions, (ii) develop numerical optimization methods from optimal control and reinforcement learning to compute optimal, feasible trajectories and policies, and (iii) design optimization-based predictive control schemes for maneuvering of autonomous systems. To bridge the gap between theory and application, students will apply the studied techniques to trajectory optimization and maneuvering of autonomous systems in a number of application domains including autonomous vehicles, intelligent robots(e.g., aerial robots) and other mechatronic systems.

Contenuti

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Testi/Bibliografia

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Metodi didattici

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Modalità di verifica e valutazione dell'apprendimento

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Strumenti a supporto della didattica

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Orario di ricevimento

Consulta il sito web di Giuseppe Notarstefano