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Fall 2018
Temporal-difference (TD) learning is an important approach for predictive knowledge representation and sequential decision making. Within TD learning exists multi-step methods which unify one-step TD learning and Monte Carlo methods in a way where intermediate algorithms can outperform either...
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Fall 2023
The ability to adaptively respond to changing environments is a fundamental aspect of intelligent behaviour. From catching a ball in motion to changing one’s mind in the face of new information, adaptation requires several key cognitive mechanisms, such as the flexible integration of sensorimotor...
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Advances in Distributional Reinforcement Learning: Bridging Theory with Algorithmic Practice
DownloadFall 2024
This thesis comprehensively investigates Distributional Reinforcement Learning~(RL), a vibrant research field that interplays between statistics and RL. As an extension of classical RL, distributional RL, on the one hand, embraces plenty of statistical ideas by incorporating distributional...
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Fall 2022
In most, if not every, realistic sequential decision-making tasks, the decision-making agent is not able to model the full complexity of the world. In reinforcement learning, the environment is often much larger and more complex than the agent, a setting also known as partial observability. In...
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Fall 2021
Reinforcement learning (RL) is a learning paradigm focusing on how agents interact with an environment to maximize cumulative reward signals emitted from the environment. Exploration versus exploitation challenge is critical in RL research: the agent ought to trade off between taking the known...
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Fall 2021
In recent years, due to the environmental concerns caused by the emissions from public transit services relying on traditional fossil fuels, the electrification of the public transit sector has attracted great attention from both automobile industry and academia. Specifically, the electric buses...
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Fall 2022
Imperfect information games model many large-scale real-world problems. Hex is the classic two-player zero-sum no-draw connection game where each player wants to join their two sides. Dark Hex is an imperfect information version of Hex in which each player sees only their own moves. Finding Nash...
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Spring 2024
The advent of Industry 4.0 integrates advanced digital technologies and Artificial Intelligence (AI) into system engineering. This research explores the potential of AI in smart automation for industries, bridging it with physics-informed approaches, particularly through Explainable Artificial...
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Ensembling Diverse Policies Improves Generalization of Deep Reinforcement Learning Algorithms to Environmental Changes in Continuous Control Tasks
DownloadFall 2023
Deep Reinforcement Learning (DRL) algorithms have shown great success in solving continuous control tasks. However, they often struggle to generalize to changes in the environment. Although retraining may help policies adapt to changes, it may be quite costly in some environments. Ensemble...
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Spring 2021
Temporal difference (TD) methods provide a powerful means of learning to make predictions in an online, model-free, and highly scalable manner. In the reinforcement learning (RL) framework, we formalize these prediction targets in terms of a (possibly discounted) sum of rewards, called the...