Browsing Theses by Subject "reinforcement learning"
Now showing items 1-20 of 41
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Adapting Regenerative Braking Strength to Driver Preference
(University of Waterloo, 2023-12-13)The modern automotive industry has witnessed a growing emphasis on adapting the driving experience to individual drivers. With the rising popularity of electrified vehicles, the implementation of regenerative braking ... -
Adversarial Machine Learning and Defenses for Automated and Connected Vehicles
(University of Waterloo, 2024-04-18)This thesis delves into the realm of adversarial machine learning within the context of Connected and Automated Vehicles (CAVs), presenting a comprehensive study on the vulnerabilities and defense mechanisms against ... -
AlphaSMT: A Reinforcement Learning Guided SMT Solver
(University of Waterloo, 2023-06-19)Satisfiability Modulo Theories (SMT) solvers are programs that decide whether a first-order logic formula is satisfiable. Over the last two decades, these solvers have become central to many methods and tools in fields as ... -
Asking for Help with a Cost in Reinforcement Learning
(University of Waterloo, 2020-05-15)Reinforcement learning (RL) is a powerful tool for developing intelligent agents, and the use of neural networks makes RL techniques more scalable to challenging real-world applications, from task-oriented dialogue systems ... -
Autonomous Driving: A Multi-Objective Deep Reinforcement Learning Approach
(University of Waterloo, 2019-05-23)Autonomous driving is a challenging domain that entails multiple aspects: a vehicle should be able to drive to its destination as fast as possible while avoiding collision, obeying traffic rules and ensuring the comfort ... -
Closing the Modelling Gap: Transfer Learning from a Low-Fidelity Simulator for Autonomous Driving
(University of Waterloo, 2020-01-24)The behaviour planning subsystem, which is responsible for high-level decision making and planning, is an important aspect of an autonomous driving system. There are advantages to using a learned behaviour planning system ... -
Deep deterministic policy gradient: applications in process control and integrated process design and control
(University of Waterloo, 2022-06-20)In recent years, the urgent need to develop sustainable processes to fight the negative effects of climate change has gained global attention and has led to the transition into renewable energies. As renewable sources ... -
Deep Reinforcement Learning Models for Real-Time Traffic Signal Optimization with Big Traffic Data
(University of Waterloo, 2021-05-19)One of the most significant changes that the globe has faced in recent years is the changes brought about by the COVID19 pandemic. While this research was started before the pandemic began, the pandemic has exposed the ... -
Designing Intelligent Energy Management and Cost-effective Data Acquisition for Vehicular Solar Idle Reduction Systems
(University of Waterloo, 2019-09-23)In this study, an innovative energy management system (EMS) employing the promising reinforcement learning (RL) method is proposed. The EMS intelligently administrates the power flow between the main battery which is fed ... -
Dynamic Resource Provisioning and Scheduling in SDN/NFV-Enabled Core Networks
(University of Waterloo, 2020-12-17)The service-oriented fifth-generation (5G) core networks are featured by customized network services with differentiated quality-of-service (QoS) requirements, which can be provisioned through network slicing enabled by ... -
A Framework for Aggregation of Multiple Reinforcement Learning Algorithms
(University of Waterloo, 2007-04-11)Aggregation of multiple Reinforcement Learning (RL) algorithms is a new and effective technique to improve the quality of Sequential Decision Making (SDM). The quality of a SDM depends on long-term rewards rather than the ... -
Hierarchical reinforcement learning in a biologically plausible neural architecture
(University of Waterloo, 2014-11-18)Humans and other animals have an impressive ability to quickly adapt to unfamiliar environments, with only minimal feedback. Computational models have been able to provide intriguing insight into these processes, by making ... -
The Impact of Teams in Multiagent Systems
(University of Waterloo, 2023-07-31)Across many domains, the ability to work in teams can magnify a group's abilities beyond the capabilities of any individual. While the science of teamwork is typically studied in organizational psychology (OP) and areas ... -
Learning a Motion Policy to Navigate Environments with Structured Uncertainty
(University of Waterloo, 2020-01-24)Navigating in uncertain environments is a fundamental ability that robots must have in many applications such as moving goods in a warehouse or transporting materials in a hospital. While much work has been done on navigation ... -
Learning Energy-Aware Transaction Scheduling in Database Systems
(University of Waterloo, 2021-09-20)Servers are typically sized to accommodate peak loads, but in practice, they remain under-utilized for much of the time. During periods of low load, there is an opportunity to save power by quickly adjusting processor ... -
Learning-Free Methods for Goal Conditioned Reinforcement Learning from Images
(University of Waterloo, 2021-04-27)We are interested in training goal-conditioned reinforcement learning agents to reach arbitrary goals specified as images. In order to make our agent fully general, we provide the agent with only images of the environment ... -
Machine Learning Techniques and Stochastic Modeling in Mathematical Oncology
(University of Waterloo, 2022-07-18)The cancer stem cell hypothesis claims that tumor growth and progression are driven by a (typically) small niche of the total cancer cell population called cancer stem cells (CSCs). These CSCs can go through symmetric ... -
Model-Based Bayesian Sparse Sampling for Data Efficient Control
(University of Waterloo, 2019-06-24)In this work, we propose a novel Bayesian-inspired model-based policy search algorithm for data efficient control. In contrast to other model-based approaches, our algorithm makes use of approximate Gaussian processes in ... -
Model-based Reinforcement Learning of Nonlinear Dynamical Systems
(University of Waterloo, 2022-01-25)Model-based Reinforcement Learning (MBRL) techniques accelerate the learning task by employing a transition model to make predictions. In this dissertation, we present novel techniques for online learning of unknown dynamics ... -
Modeling human-coupled common pool resource systems with techniques in evolutionary game theory and reinforcement learning
(University of Waterloo, 2021-05-26)Shared resource extraction among profit-seeking individuals involves a tension between individual benefit and the collective well-being represented by the persistence of the resource. In these systems, the decisions of ...