Optimal and Learning Control for Autonomous Robots
This is an incremental educational resource for robotics students at ETH Zurich, with no new research contributions.
The paper presents lecture notes for a course that teaches optimal control and reinforcement learning from a unified perspective, aimed at supporting students in understanding these methods for autonomous robots.
Optimal and Learning Control for Autonomous Robots has been taught in the Robotics, Systems and Controls Masters at ETH Zurich with the aim to teach optimal control and reinforcement learning for closed loop control problems from a unified point of view. The starting point is the formulation of of an optimal control problem and deriving the different types of solutions and algorithms from there. These lecture notes aim at supporting this unified view with a unified notation wherever possible, and a bit of a translation help to compare the terminology and notation in the different fields. The course assumes basic knowledge of Control Theory, Linear Algebra and Stochastic Calculus.