imports.py
Learning-based Control
Introduction
Lectures
Lecture 1: learning-based control
Lecture 2: general-purpose algorithms
Lecture 3: descent algorithms
Lecture 4: nonlinear programming
Lecture 5: dynamic programming
Lecture 6: linear quadratic regulation
Lecture 7: constrained nonlinear programming
Lecture 8: trajectory optimization
Lecture 9: randomness
Lecture 10: Markov processes
Lecture 11: Solution of finite MP
Lecture 12: Markov decision processes (MDP)
Lecture 13: Bellman equation for finite MDP
Lecture 14: Bellman operators for finite MDP
Lecture 15: model-based algorithms for finite MDP
Lecture 16: model-free algorithms for finite MDP
Lecture 17: function approximation
Lecture 18: policy gradients
Lecture 19: control barrier functions
Lecture 20: system-level synthesis
Lecture 21: model-predictive control
Homeworks
Homework 0
Homework 1
Homework 2
Homework 3
imports.py
Homework 1 solution
Homework 2 solution
Homework 3 solution
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$$ \newcommand{\R}{\mathbb{R}} \newcommand{\N}{\mathbb{N}} \newcommand{\Z}{\mathbb{Z}} $$
imports.py
Standard imports and utilities used across lectures and homeworks.
Download imports.py
Homework 3
Homework 1 solution