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Skip to Search Results- 83Reinforcement Learning
- 17Machine Learning
- 8Artificial Intelligence
- 6Transfer Learning
- 5Planning
- 5Representation Learning
- 1Abbasi Brujeni, Lena
- 1Abbasi-Yadkori, Yasin
- 1Aghakasiri, Kiarash
- 1Alikhasi, Mahdi
- 1Asadi Atui, Kavosh
- 1Banafsheh Rafiee
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Fall 2022
We have witnessed the rising popularity of real-world applications of reinforcement learning (RL). However, most successful real-world applications of RL rely on high-fidelity simulators that enable rapid iteration of prototypes, hyperparameter selection and policy training. On the other hand, RL...
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Fall 2020
Communication is essential for coordination among humans and animals. Therefore, with the introduction of intelligent agents into the world, agent-to-agent and agent-to-human communication become necessary. Ideally, these agents should be trained in an incremental and decentralized manner. In...
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Fall 2022
This thesis targets output tracking problem for payload position and quadrotor yaw in an slung load system (SLS). In spite of its relatively extensive literature, full SLS control is still a challenging problem since its dimension, nonlinearity, and multiple sources of disturbances are not easy...
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Spring 2021
This thesis investigates the use of general value functions for detecting anomalous behavior in machines. Identifying abnormal behavior is critical for ensuring the safety and reliability of any machine or industrial process. When the cause of these anomalies is due to accumulated wear on...
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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...