16-745: Optimal Control and Reinforcement Learning

Winter 2023

16-745 Final Presentation

My final project for OCRL (Optimal Control and Reinforcement Learning) was making a one-legged hopper with three reaction wheels successfully balance. This involved writing a control policy that took the full state of the robot into account and sent a torque command to the reaction wheels without exceeding the maximum torque/RPM of the motors.

Code: https://github.com/sofiakwok/mujoco_LQR

Course description: This course surveys the use of optimization to design behavior. We will explore ways to represent policies including hand-designed parametric functions, basis functions, tables, and trajectory libraries. We will also explore algorithms to create policies including parameter optimization and trajectory optimization (first and second order gradient methods, sequential quadratic programming, random search methods, evolutionary algorithms, etc.). We will discuss how to handle the discrepancy between models used to create policies and the actual system being controlled (evaluation and robustness issues). The course will combine lectures, student-presented material, and projects. The goal of this course will be to help participants find the most effective methods for their problems.

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