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Fall 2023

Meyer, Edan J

In reinforcement learning (RL), agents learn to maximize a reward signal using nothing but observations from the environment as input to their decision making processes. Whether the agent is simple, consisting of only a policy that maps observations to actions, or complex, containing auxiliary...

Spring 2020

Jafferjee, Taher

In Dyna style algorithms, reinforcement learning (RL) agents use a model of the environment to generate simulated experience. By updating on this simulated experience, Dyna style algorithms allow agents to potentially learn control policies in fewer environment interactions than agents that use...

Fall 2023

He, Jiamin

Off-policy policy evaluation has been a critical and challenging problem in reinforcement learning, and Temporal-Difference (TD) learning is one of the most important approaches for addressing it. There has been significant interest in searching for off-policy TD algorithms which find the same...

Fall 2021

McLeod, Matthew

Learning auxiliary tasks, such as multiple predictions about the world, can provide many benets to reinforcement learning systems. A variety of off-policy learning algorithms have been developed to learn such predictions, but as yet there is little work on how to adapt the behavior to gather...

Fall 2023

Mitchell, Daniel

Multilevel action selection is a reinforcement learning technique in which an action is broken into two parts, the type and the parameters. When using multilevel action selection in reinforcement learning, one must break the action space into multiple subsets. These subsets are typically disjoint...

Fall 2016

Johnson, Stuart H

Current medical imaging professional training uses an apprenticeship model with students following an established doctor and viewing their cases, in what is called a practicum. This posses an issue as students are limited to the cases available during their practicum. To resolve this automated...

Spring 2024

Zhang, Tianyu

Retrofitting buildings and optimizing their operation have been at the forefront of global efforts to reduce carbon emissions over the past few decades. Intelligent control of building systems, such as Heating, Ventilation, and Air Conditioning (HVAC), presents two clear benefits: it improves...

Fall 2023

Karimi, Amirmohammad

In classic reinforcement learning(RL) for continuous control, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter. Setting it too short may increase the problem’s difficulty by requiring the agent to make numerous decisions...

Spring 2023

Rahman, Saidur

Virtual power plants (VPPs) can enhance reliability and efficiency of power systems with a high share of renewables. However, their adoption largely depends on their profitability, which is difficult to maximize due to the heterogeneity of their components, different sources of uncertainty and...

Spring 2023

Wang, Yan

Vision is one of the essential means for humans to perceive the world. Similarly, today's intelligent robot agents rely on camera images to perform complex tasks in the real world. Due to the ever-changing nature of the real world, intelligent robot agents must continually learn from...