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Fall 2021

Pan, Yangchen

A common scientific challenge for putting a reinforcement learning agent into practice is how to improve sample efficiency as much as possible with limited computational or memory resources. Such available physical resources may vary in different applications. My thesis introduces some approaches...

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 2016

Yao,Hengshuai

In model-based reinforcement learning a model is learned which is then used to find good actions. What model to learn? We investigate these questions in the context of two different approaches to model-based reinforcement learning. We also investigate how one should learn and plan when the reward...

Spring 2014

Bastani, Meysam

Each patient with Type-1 diabetes must decide how much insulin to inject before each meal to maintain an acceptable level of blood glucose. The actual injection dose is based on a formula that takes current blood glucose level and the meal size into consideration. While following this insulin...

Spring 2024

Gomez Noriega, Diego Fernando

The ability to learn good representations of states is essential for solving large reinforcement learning problems, where exploration, generalization, and transfer are particularly challenging. The Laplacian representation is a promising approach to address these problems by inducing intrinsic...

Fall 2009

Silver, David

Learning and planning are two fundamental problems in artificial intelligence. The learning problem can be tackled by reinforcement learning methods, such as temporal-difference learning, which update a value function from real experience, and use function approximation to generalise across...

Spring 2024

Rafiee, Banafsheh

In reinforcement learning, the notion of state plays a central role. A reinforcement learning agent requires the state to evaluate its current situation, select actions, and construct a model of the environment. In the classic setting, it is assumed that the environment provides the agent with...

Spring 2012

Ávila Pires, Bernardo

We study linear estimation based on perturbed data when performance is measured by a matrix norm of the expected residual error, in particular, the case in which there are many unknowns, but the “best” estimator is sparse, or has small L1-norm. We propose a Lasso-like procedure that finds the...

Fall 2017

Vasan, Gautham

The idea of an amputee playing the piano with all the flair and grace of an able-handed person may seem like a futuristic fantasy. While many prosthetic limbs look lifelike, finding one that also moves naturally has proved more of a challenge for both researchers and amputees. Even though...

Spring 2024

Liu, Vincent

Offline reinforcement learning (RL) involves learning policies from datasets, rather than online interaction. The dissertation first investigates a critical component in offline RL: offline policy selection (OPS). Given that most offline RL algorithms require careful hyperparameter tuning, we...