Search

Fall 2010

Mahmood, Ashique

Performance and stability of many iterative algorithms such as stochastic gradient descent largely depend on a fixed and scalar step-size parameter. Use of a fixed and scalar step-size value may lead to limited performance in many problems. We study several existing step-size adaptation...

Fall 2017

Mahmood, Ashique

Model-free off-policy temporal-difference (TD) algorithms form a powerful component of scalable predictive knowledge representation due to their ability to learn numerous counter- factual predictions in a computationally scalable manner. In this dissertation, we address and overcome two...

Spring 2019

Young, Kenneth

In realistic environments, intelligent agents must learn to integrate information from their past to inform present decisions. An agent's immediate observations are often limited, and some degree of memory is necessary to complete many everyday tasks. However, an agent cannot remember everything...

Spring 2024

Young, Kenneth J.

In this dissertation, I investigate how we can exploit generic problem structure to make reinforcement learning algorithms more efficient. Generic problem structure means basic structure that exists in a wide range of problems (e.g., an action taken in the present does not influence the past), as...

Spring 2024

Naik, Abhishek

This dissertation develops simple and practical learning algorithms from first principles for long-lived agents. Formally, the algorithms are developed within the reinforcement learning framework for continuing (non-episodic) problems, in which the agent-environment interaction goes on ad...

Spring 2021

Ni, Jingjiao

Emphatic-Temporal-Difference (Emphatic-TD) learning algorithms were recently proposed based on the most central and widely used reinforcement learning algorithms, Temporal-Difference (TD) methods. Emphatic-TD learning algorithms were originally designed to solve the divergence problem of...

Fall 2020

Shibhansh Dohare

We explore the interplay of generate-and-test and gradient-descent techniques for solving online supervised learning problems. The task in supervised learning is to learn a function using samples of inputs to output pairs. This function is called the target function. The standard way to learn...

Fall 2015

Sherstan, Craig

The control of powered prosthetic arms has been researched for over 50 years, yet prosthetic control remains an open problem, not just from a research perspective, but from a clinical perspective as well. Significant advances have been made in the manufacture of highly functional prosthetic...

Spring 2018

Jeya Veeraiah, Vivek Veeriah

Facial expressions and other body language are important for human commu- nication. They complement speech and make the process of communication simple and sustainable. However, the process of communication using existing approaches to human-machine interaction is not intuitive as that of human...