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Spring 2011
Off-policy reinforcement learning is useful in many contexts. Maei, Sutton, Szepesvari, and others, have recently introduced a new class of algorithms, the most advanced of which is GQ(lambda), for off-policy reinforcement learning. These algorithms are the first stable methods for general...
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Fall 2023
Of all the capabilities of natural intelligence, one of the most exceptional is the ability to expand upon and refine knowledge of the world through subjective experience. Therefore, a longstanding goal of Artificial Intelligence has been to replicate this success: to enable artificial agents to...
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Spring 2015
This thesis consists of two independent projects, each contributing to a central goal of artificial intelligence research: to build computer systems that are capable of performing tasks and solving problems without problem-specific direction from us, their designers. I focus on two formal...
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Strengths, Weaknesses, and Combinations of Model-based and Model-free Reinforcement Learning
DownloadSpring 2016
Reinforcement learning algorithms are conventionally divided into two approaches: a model-based approach that builds a model of the environment and then computes a value function from the model, and a model-free approach that directly estimates the value function. The first contribution of this...
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Fall 2020
This thesis is offered as a step forward in our understanding of forgetting in artificial neural networks. ANNs are a learning system loosely based on our understanding of the brain and are responsible for recent breakthroughs in artificial intelligence. However, they have been reported to be...