Browsing Theses by Subject "reinforcement learning"
Now showing items 21-40 of 41
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Multi-Agent Reinforcement Learning in Large Complex Environments
(University of Waterloo, 2022-07-15)Multi-agent reinforcement learning (MARL) has seen much success in the past decade. However, these methods are yet to find wide application in large-scale real world problems due to two important reasons. First, MARL ... -
Navigating Unsignalized Intersections: Deep RL-Based Decision-Making and Control Framework for Autonomous Vehicles with Pedestrian Integration
(University of Waterloo, 2024-04-25)Unprotected left turns at unsignalized intersections, alongside pedestrians and adversarial vehicles, pose significant challenges for Autonomous Vehicle (AV)s. These challenges stem from the absence of traffic signals or ... -
Neural Text Generation from Structured and Unstructured Data
(University of Waterloo, 2019-08-28)A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures. In particular, several recent papers have shown that simpler, properly tuned models are at least ... -
A New Approach to Reinforcement Learning for Sequential Robotic Tasks using a Chained Options Model and Subtask-Focused Rewards
(University of Waterloo, 2021-09-09)Reinforcement Learning for Robotics is a trending area of research with tremendous potential for widescale industry adoption. To its detriment, large amounts of environmental interactions are typically required by robotic ... -
On the Orchestration and Provisioning of NFV-enabled Multicast Services
(University of Waterloo, 2020-05-14)The paradigm of network function virtualization (NFV) with the support of software-defined networking has emerged as a prominent approach to foster innovation in the networking field and reduce the complexity involved in ... -
Optimal Learning Theory and Approximate Optimal Learning Algorithms
(University of Waterloo, 2019-09-12)The exploration/exploitation dilemma is a fundamental but often computationally intractable problem in reinforcement learning. The dilemma also impacts data efficiency which can be pivotal when the interactions between the ... -
Optimization of Policy Evaluation and Policy Improvement Methods in Portfolio Optimization using Quasi-Monte Carlo Methods
(University of Waterloo, 2024-05-24)Machine learning involves many challenging integrals that can be estimated using numerical methods. One application of these methods which has been explored in recent work is the estimation of policy gradients for ... -
Optimizing EV Routing and Charging/Discharging under Time-Variant Electricity Prices
(University of Waterloo, 2020-08-19)The integration of electric vehicles (EVs) and the power system has been becoming an increasingly important field of research, due to the rapid EV penetration and the evolvement in vehicle-to-grid (V2G) techniques in the ... -
Prediction and Planning in Dynamical Systems with Underlying Markov Decision Processes
(University of Waterloo, 2021-08-24)Predicting the future state of a scene with moving objects is a task that humans handle with ease. This is due to our understanding about the dynamics of the objects in the scene and the way they interact. However, teaching ... -
Recovering Optimal Cost Functions for Natural Walking: From Musculoskeletal Simulation to Exoskeleton Control
(University of Waterloo, 2022-05-03)Human movement studies have contributed to our understanding of how the central nervous system's (CNS) interactions with our body results in rich and complex motor behaviours, such as human gait. Such understanding is ... -
Reinforcement Learning for Parameter Control of Image-Based Applications
(University of Waterloo, 2004)The significant amount of data contained in digital images present barriers to methods of learning from the information they hold. Noise and the subjectivity of image evaluation further complicate such automated processes. ... -
Reinforcement Learning of Dynamic Collaborative Driving
(University of Waterloo, 2008-05-21)Dynamic Collaborative Driving is the concept of decentralized multi-vehicle automated driving where vehicles form dynamic local area networks within which information is shared to build a dynamic data representation of the ... -
RELPH: A Computational Model for Human Decision Making
(University of Waterloo, 2013-09-19)The updating process, which consists of building mental models and adapting them to the changes occurring in the environment, is impaired in neglect patients. A simple rock-paper-scissors experiment was conducted in our ... -
Robotic Reach, Grasp, and Pick-and-Place using Combined Reinforcement Learning and Traditional Controls
(University of Waterloo, 2022-09-01)Electrically actuated robotic arms have been implemented to complete tasks which are repetitive, strenuous, and/or dangerous since they were first developed in the 1970s. More than 50 years have passed since initial ... -
Safety-Oriented Stability Biases for Continual Learning
(University of Waterloo, 2020-01-24)Continual learning is often confounded by “catastrophic forgetting” that prevents neural networks from learning tasks sequentially. In the case of real world classification systems that are safety-validated prior to ... -
Socially and Spatially Aware Motion Prediction of Dynamic Objects for Autonomous Driving
(University of Waterloo, 2023-03-17)The primary goal of this thesis project is to develop a robust object motion prediction framework enabling safe decision making for autonomous vehicles in various driving scenarios. Given the comparatively higher importance ... -
Spatially-Distributed Interactive Behaviour Generation for Architecture-Scale Systems Based on Reinforcement Learning
(University of Waterloo, 2020-02-18)This thesis is part of the research activities of the Living Architecture System Group (LASG). LASG develops immersive, interactive art sculptures combining concepts of architecture, art, and electronics which allow occupants ... -
A spiking neural network of state transition probabilities in model-based reinforcement learning
(University of Waterloo, 2017-10-23)The development of the field of reinforcement learning was based on psychological studies of the instrumental conditioning of humans and other animals. Recently, reinforcement learning algorithms have been applied to ... -
Stackelberg Multi-Agent Reinforcement Learning for Hierarchical Environments
(University of Waterloo, 2020-05-14)This thesis explores the application of multi-agent reinforcement learning in domains containing asymmetries between agents, caused by differences in information and position, resulting in a hierarchy of leaders and ... -
Storage System Management Using Reinforcement Learning Techniques and Nonlinear Models
(University of Waterloo, 2009-01-21)In this thesis, modeling and optimization in the field of storage management under stochastic condition will be investigated using two different methodologies: Simulation Optimization Techniques (SOT), which are usually ...