Planning and Learning in Robotics

4 credits

This course covers optimal control and reinforcement learning fundamentals and their application to planning and decision-making in mobile robotics. Topics include Markov decision processes (MDP), Pontryagin’s maximum principle, linear quadratic regulation (LQR), deterministic planning, value and policy iteration, and policy gradient methods. Prerequisites: ECE 276A with a grade of B– or above; graduate standing.