Browsing by Subject "Reinforcement Learning"
Now showing items 1-20 of 38
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(2020-11-30)High integration of intermittent renewable energy sources (RES), specifically wind power, has created complexities in power system operations due to their limited controllability and predictability. In addition, large ...
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(2022-12-08)Energy infrastructures are mission critical cyber-physical systems that are targets of persistent cyber attacks. While introducing new computing technologies and networks in power systems adds new capabilities for monitoring ...
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(2021-07-14)With the recent advances in satellite miniaturization, communication and information technologies, there has been a paradigm shift in space exploration missions over the last few decades. This paradigm shift involves the ...
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(2022-12-09)Anomaly detection, which aims to identify unusual or uncommon behaviors in data, has many real-world applications. While numerous machine learning algorithms have been developed for anomaly detection, we often still need ...
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(2020-11-17)Automatic Feature Engineering (AFE) aims to extract useful knowledge for interpretable predictions given data for the machine learning tasks of interest. Here, we develop AFE to extract dependency relationships that can ...
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(2023-04-25)Four stochastic inversion and optimization methods (MCMC, SA, PSO, and ACO) are investigated and compared by coupling with a novel mechanistic model for subsurface characterization to jointly estimate the subsurface ...
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(2020-10-27)Drug resistance is a fundamental barrier to developing robust antimicrobial and anticancer therapies. Its first sign was observed in the 1940s, soon after discovering penicillin, the first modern antibiotic. This dissertation ...
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(2022-03-04)In our modern world where everyone is always connected through internet, terabytes of data gets generated at every moment through online activities like communication on social media, on-line banking, online shopping, ...
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(2023-07-26)We first consider dynamic priority resource allocation for media streaming applications at a wireless edge (access) network. We realize that by formulating the policy design question as a CMDP, and decompose it into ...
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(2022-06-22)The problem of Reinforcement Learning (RL) is equivalent to the search for an optimal feedback control policy from data without system dynamics information. Most RL techniques search over a complex global nonlinear ...
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(2009-05-15)This dissertation develops and evaluates a new adaptive traffic signal control system for arterials. This control system is based on reinforcement learning, which is an important research area in distributed artificial ...
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(2022-04-21)Operational decision-making during drilling for hydrocarbons or geothermal energy is challenging due to the complex nature of the process. Many of the times, these decisions have to be taken with incomplete information at ...
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(2020-07-30)Monte Carlo REINFORCE is used to design an algorithm to not only find the optimal deep learning architecture but also the optimal set of features that can maximize the performance of the said deep learning model. The ...
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(2023-05-01)This work proposes an approach to optimizing media streaming performance using reinforcement learning (RL) algorithms. The proposed approach leverages RL’s ability to learn from experience to adapt streaming policies in ...
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(2017-04-27)Recently, many companies have been studying intelligent cars, and improvements in sensor technology and computing are required. The intelligent cars use GPS to know where they are. The cars use sensors to detect objects ...
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(2023-07-07)Reinforcement learning is a powerful approach for training intelligent agents to make decisions in complex environments. However, these algorithms often struggle when faced with challenging scenarios, such as sparse reward ...
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(2019-02-25)Demand for learning, design and decision making is higher than ever before. Autonomous vehicles need to learn how to ride safely by recognizing pedestrians, traffic signs, and other cars. Companies and consumers need to ...
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With the rise of reinforcement learning, a number of physics engines and frameworks have been used to simulate virtual environments. Additionally, various benchmarks have been popularized in order to assess the ability of ...
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(2021-07-14)Artificial intelligence (AI) is revolutionizing various systems within the Architecture, Engineering, Construction, and Facilities Management (AEC/FM) domains. The rapid advancements in computational methods, engineering ...
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(2020-03-17)Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. This can be attributed to the fact that ...