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Spring 2020

Schumacker Soares, Everton

Several Artificial Intelligence (AI) techniques such as machine learning, evolutionary computing, and Artificial Life (A-life) have been increasingly used to generate emergence of novel behaviours in multi-agent simulations (e.g., commercial games). However, automatically detecting emergent...

Spring 2021

Bennett, Brendan

Temporal difference (TD) methods provide a powerful means of learning to make predictions in an online, model-free, and highly scalable manner. In the reinforcement learning (RL) framework, we formalize these prediction targets in terms of a (possibly discounted) sum of rewards, called the...

Fall 2024

De Asis, Kris

Value-based reinforcement learning is an approach to sequential decision making in which decisions are informed by learned, long-horizon predictions of future reward. This dissertation aims to understand issues that value-based methods face and develop algorithmic ideas to address these issues....

Fall 2020

Chan, Alan

Policy gradient methods typically estimate both explicit policy and value functions. The long-extant view of policy gradient methods as approximate policy iteration---alternating between policy evaluation and policy improvement by greedification---is a helpful framework to elucidate algorithmic...

Fall 2023

Davis, Sarah (Sacha) Maren

The rate of 30-day hospital readmission is a common measurement of hospital quality, which can affect the funding a hospital receives. Over a quarter of readmissions are estimated to be preventable with adequate interventions, but these interventions are themselves costly. For this reason, many...

Spring 2023

Ghosh, Shrimanti

In locally advanced cervical cancer (LACC), brachytherapy (BT) remains the gold standard for boosting to curative doses in radiotherapy. Progress towards balancing target and routine tissue dosimetry for better clinical outcomes has been made possible by magnetic resonance imaging (MRI)-guided...

Fall 2023

Ady, Nadia Michelle

Curiosity appears to motivate and guide effective learning in humans, which has led to high hopes in the machine learning community for machine analogues of curiosity. While a variety of machine curiosity algorithms have been introduced, they are rarely compared with other existing curiosity...