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- 83Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
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Fall 2021
Unmanned Aerial Vehicles (UAVs), or drones, have been employed in a variety of applications, ranging from surveillance to emergency operations. These systems comprise an ”inner loop” that provides stability and control and an ”outer loop” in charge of mission-level tasks, such as way-point...
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Fall 2022
Internal Combustion Engines (ICEs) are ubiquitous; they power a wide range of systems. The broad use of ICEs globally causes more than 20% of the total greenhouse gas emissions. In many countries, emission legislation is transitioning from certification using only traditional chassis dynomometer...
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Spring 2022
Reinforcement learning (RL) has shown great success in solving many challenging tasks via the use of deep neural networks. Although the use of deep learning for RL brings immense representational power to the arsenal, it also causes sample inefficiency. This means that the algorithms are...
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Fall 2022
Monte Carlo Tree Search (MCTS) is a popular tree search framework for choos- ing actions in decision-making problems. MCTS is traditionally applied to applications in which a perfect simulation model is available. However, when the model is imperfect, the performance of MCTS drops heavily. In...
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Fall 2022
Monte Carlo Tree Search (MCTS) is an extremely successful search-based frame- work for decision making. With an accurate simulator of the environment’s dynamics, it can achieve great performance in many games and non-games applications. However, without a perfect simulator, the performance...
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Fall 2021
The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, testing different hyperparameter configurations directly on the environment can be financially prohibitive, dangerous,...
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Fall 2021
The optimization of non-convex objective functions is a topic of central interest in machine learning. Remarkably, it has recently been shown that simple gradient-based optimization can achieve globally optimal solutions in important non-convex problems that arise in machine learning, including...
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On Efficient Planning in Large Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
DownloadFall 2023
A practical challenge in reinforcement learning is large action spaces that make planning computationally demanding. For example, in cooperative multi-agent reinforcement learning, a potentially large number of agents jointly optimize a global reward function, which leads to a blow-up in the...
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On the Application of Continuous Deterministic Reinforcement Learning in Neural Architecture Search
DownloadSpring 2021
Architecture evaluation is a major bottleneck of Neural Architecture Search (NAS). Recent trends have seen a shift in favor of weight-sharing networks capable of superimposing all possible candidate architectures in a search space. Nevertheless, this technique is not beyond reproach, and has...
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Spring 2024
In machine learning, sparse neural networks provide higher computational efficiency and in some cases, can perform just as well as fully-connected networks. In the online and incremental reinforcement learning (RL) problem, Prediction Adapted Networks (Martin and Modayil, 2021) is an algorithm...