Browsing by Subject "Reinforcement Learning"
Now showing items 1-7 of 7
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Feature Selection for Value Function Approximation
(2011)The field of reinforcement learning concerns the question of automated action selection given past experiences. As an agent moves through the state space, it must recognize which state choices are best in terms of allowing ... -
Innovations in Decompression Sickness Prediction and Adaptive Ascent Algorithms
(2023)Decompression Sickness (DCS) is a potentially serious medical condition which can occur in humans when there is a decrease in ambient pressure. While it is generally accepted that DCS is initiated by the formation and growth ... -
Locally Adaptive Protocols for Quantum State Discrimination
(2021)This dissertation makes contributions to two rapidly developing fields: quantum information theory and machine learning. It has recently been demonstrated that reinforcement learning is an effective tool for a wide variety ... -
Model-based Reinforcement Learning in Modified Levy Jump-Diffusion MarkovDecision Model and Its Financial Applications
(2017-11-15)This thesis intends to address an important cause of the 2007-2008 financial crisis by incorporating prediction on asset pricing jumps in asset pricing models, the non-normality of asset returns. Several different machine ... -
Nonlinear Energy Harvesting With Tools From Machine Learning
(2020)Energy harvesting is a process where self-powered electronic devices scavenge ambient energy and convert it to electrical power. Traditional linear energy harvesters which operate based on linear resonance work well only ... -
PAC-optimal, Non-parametric Algorithms and Bounds for Exploration in Concurrent MDPs with Delayed Updates
(2015)As the reinforcement learning community has shifted its focus from heuristic methods to methods that have performance guarantees, PAC-optimal exploration algorithms have received significant attention. Unfortunately, the ... -
Towards Uncertainty and Efficiency in Reinforcement Learning
(2021)Deep reinforcement learning (RL) has received great success in playing video games and strategic board games, where a simulator is well-defined, and massive samples are available. However, in many real-world applications, ...
