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A Stochastic Distributed Control Allocation Method Using Probability Collectives
(2016-12-08)
The configuration of the aerospace vehicle of tomorrow marches forward in complexity in step with technological advances in computation, materials, propulsion, and beyond. One of many attributes resulting from this evolution ...
PILOT DEMONSTRATION BASED REINFORCEMENT LEARNING WITH APPLICATION TO LOW SPEED AIRSHIP CONTROL
(2017-01-12)
Designing control systems for airship has unique challenges as compared to conventional aircraft. Highly nonlinear dynamics, different mass/inertia relations, vast uncertainties in the model parameters and underactuation ...
ADVANCING THE RADIATION ONCOLOGY CLINIC WITH MOTION MANAGEMENT AND AUTOMATIC TREATMENT PLANNING
(2022-06-09)
The leading cause of premature death (death under the age of 70) is cancer. The top five cancers for both male and female are: lung, colorectum, pancreas, breast cancer, and prostate. In 2020 there was an estimated 19.3 ...
Deep Reinforcement Learning-based Portfolio Management
(2019-05-16)
Machine Learning is at the forefront of every field today. The subfields of Machine Learning called Reinforcement Learning and Deep Learning, when combined have given rise to advanced algorithms which have been successful ...
Optimal control strategies and reinforcement learning for dynamical multiagent systems in graphical games
(2019-07-02)
As the number of autonomous agents increases in industrial and urban areas, the development of formal protocols to analyze their behavior as they interact with each other becomes of central interest in control systems ...
LONG-DISTANCE AND BROAD-BAND AERIAL COMMUNICATION USING DIRECTIONAL ANTENNAS: THEORY, IMPLEMENTATION, AND APPLICATIONS
(2019-08-05)
Unmanned aerial vehicles (UAV) have found broad civilian applications. However, existing commercial usages are limited to single UAVs. To facilitate commercial multi-UAV applications, robust UAV-to-UAV communication with ...
Learning Representations Using Reinforcement Learning
(2019-05-09)
The framework of reinforcement learning is a powerful suite of algorithms that can learn generalized solutions to complex decision making problems. However, the applications of reinforcement learning algorithms to traditional ...
DATA-DRIVEN DECISION MAKING AND CONTROL OF RATIONAL AGENTS
(2021-05-07)
This dissertation studies the problem of data-driven optimal decision making. The 4main contributions of this work are listed here. First, we develop a model-based and data-driven techniques for learning the cost of an ...
LEARNING TRANSFERABLE META-POLICIES FOR HIERARCHICAL TASK DECOMPOSITION AND PLANNING COMPOSITION
(2019-12-16)
In real world scenarios where situated agents are faced with dynamic, high-dimensional, partially observable environments with action and reward uncertainty, the traditional states space Reinforcement Learning (RL) becomes ...
INTRINSIC CURIOSITY IN REINFORCEMENT LEARNING BY IMPROVING NEXT STATE PREDICTION
(2020-06-03)
In Reinforcement Learning, an agent receives feedback from the environment in the form of an extrinsic reward. It learns to take actions that maximize this extrinsic reward. However, to start learning, the agent needs to ...