Variance Reduction Methods for High-Dimensional Optimal Control Problems Público

Abstract

We evaluate three variance reduction methods for solving high-dimensional optimal control problems: stochastic variance reduced gradient (SVRG), streaming stochastic variance reduced gradient (SSVRG), and stochastic recursive gradient (SARAH) algorithms. SVRG has recently been popular for finite-sum optimization; however, it is unclear whether SVRG can be applied to high-dimensional optimal control problems in real-time settings. We modify SVRG to solve the high-dimensional online optimization problem with an infinite dataset. SSVRG and SARAH are two variants of SVRG. While SSVRG is another streaming version of SVRG, SARAH leverages past stochastic gradient information to minimize variance. On several multi-agent collision avoiding problems, we fine-tune and compare the performance of SVRG, SSVRG, and SARAH with ADAM, a state-of-art algorithm. Our numerical experiments demonstrate the effectiveness of variance reduction methods for non-convex, high-dimensional optimal control problems. In particular, SARAH has the best performance in terms of convergence rate, sampling efficiency, and solution optimality. However, compared with SARAH, SVRG and SSVRG are less stable and relatively less adaptive to hyperparameter changes.

Table of Contents

1 Introduction

----1.1 Motivation

----1.2 Contributions and Outline

2 High-Dimensional Optimization 

----2.1 High-Dimensional Optimal Control

--------2.1.1 Overview

--------2.1.2 Optimal Control Problem

--------2.1.3 Multi-Agent Control Problem

--------2.1.4 Main Formulation

--------2.1.5 Numerical Implementation

----2.2 Numerical Optimization Algorithms 

--------2.2.1 Stochastic Gradient Descent (SGD)

--------2.2.2 Adaptive Learning Methods (ADAM)

--------2.2.3 Variance-Reduced Methods

----2.3 Online Optimization 

3 Variance Reduction Methods 

----3.1 Variance Reduction Basics 

----3.2 Stochastic Variance Reduced Gradient (SVRG) 

----3.3 Streaming Stochastic Variance Reduced Gradient (SSVRG) 

----3.4 Stochastic Recursive Gradient Algorithm (SARAH) 

----3.5 Implementation

4 Numerical Experiments 

----4.1 Experiment Set-Up 

----4.2 HyperParameter Optimization 

----4.3 Control Problems 

--------4.3.1 Corridor Experiment

--------4.3.2 2-agent Swap Experiment

--------4.3.3 12-agent Swap Experiment

5 Discussion 

6 Conclusion 

About this Honors Thesis

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School	Emory College
Department	Mathematics and Computer Science
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Mathematics Computer Science Applied Mathematics
Palabra Clave	variance reduction stochastic optimization optimal control online learning non-convex optimization high-dimensional control
Committee Chair / Thesis Advisor	Ruthotto, Lars, Emory University
Committee Members	Jacobson, Jeremy, Emory University Wang, Yiran, Emory University

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