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Interrelating Prediction and Control Objectives in Episodic Actor-Critic Reinforcement Learning
DownloadFall 2020
The reinforcement learning framework provides a simple way to study computational intelligence as the interaction between an agent and an environment. The goal of an agent is to accrue as much reward as possible by intelligently choosing actions given states. This problem of finding a policy that...
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Fall 2021
Powered by advancements of information and Internet technologies, there has been a rapid development in network based applications in recent years. Meanwhile, it is recognized that more attentions need to be paid to the issue of cybersecurity. The security of the network environment plays a vital...
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Spring 2024
We introduce the background of the natural language processing field, outlining the benefits and drawbacks of rule-based versus statistical methods. We present knowledge graphs as a way to integrate the explainability of rule-based methods and the power of statistical methods, large language...
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Fall 2023
The average-reward formulation is a natural and important formulation of learning and planning problems, yet has received much less attention than the episodic and discounted formulations. This dissertation makes three areas of contributions to algorithms and their theories concerning the...
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Spring 2023
Oblique decision trees use linear combinations of features in the decision nodes. Due to the non-smooth structure of decision trees, training oblique decision trees is considerably difficult as the parameters are tuned using expensive non-differentiable optimization techniques or found by...
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Learning What to Remember: Strategies for Selective External Memory in Online Reinforcement Learning Agents
DownloadSpring 2019
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...
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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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Fall 2023
Partial observability---when the senses lack enough detail to make an optimal decision---is the reality of any decision making agent acting in the real world. While an agent could be made to make due with its available senses, taking advantage of the history of senses can provide more context and...
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2011
Technical report TR11-04. World model is very important for model-based reinforcement learning. For example, a model is frequently used in Dyna: in learning steps to select actions and in planning steps to project sampled states or features. In this paper we propose least-squares Dyna (LS-Dyna)...