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Full-Text Articles in Engineering
A Reinforcement Learning Approach To Spacecraft Trajectory Optimization, Daniel S. Kolosa
A Reinforcement Learning Approach To Spacecraft Trajectory Optimization, Daniel S. Kolosa
Dissertations
This dissertation explores a novel method of solving low-thrust spacecraft targeting problems using reinforcement learning. A reinforcement learning algorithm based on Deep Deterministic Policy Gradients was developed to solve low-thrust trajectory optimization problems. The algorithm consists of two neural networks, an actor network and a critic network. The actor approximates a thrust magnitude given the current spacecraft state expressed as a set of orbital elements. The critic network evaluates the action taken by the actor based on the state and action taken. Three different types of trajectory problems were solved, a generalized orbit change maneuver, a semimajor axis change maneuver, …
An Application Of Sliding Mode Control To Model-Based Reinforcement Learning, Aaron Thomas Parisi
An Application Of Sliding Mode Control To Model-Based Reinforcement Learning, Aaron Thomas Parisi
Master's Theses
The state-of-art model-free reinforcement learning algorithms can generate admissible controls for complicated systems with no prior knowledge of the system dynamics, so long as sufficient (oftentimes millions) of samples are available from the environ- ment. On the other hand, model-based reinforcement learning approaches seek to leverage known optimal or robust control to reinforcement learning tasks by mod- elling the system dynamics and applying well established control algorithms to the system model. Sliding-mode controllers are robust to system disturbance and modelling errors, and have been widely used for high-order nonlinear system control. This thesis studies the application of sliding mode control …
Long-Distance And Broad-Band Aerial Communication Using Directional Antennas: Theory, Implementation, And Applications, Songwei Li
Electrical Engineering Dissertations
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 long-distance and broad-band capabilities is critical. Such a communication architecture should not rely on ground infrastructure support, and hence can be applied whenever needed in scenarios where infrastructures are not available. It is foreseen to have extensive usages in a wide spectrum of commercial UAV applications such as emergency response, remote sensing, and nondestructive health monitoring. A promising solution for such a communication architecture is aerial communication using directional antennas (ACDA). We developed …
Optimal Control Strategies And Reinforcement Learning For Dynamical Multiagent Systems In Graphical Games, Victor Gabriel Lopez Mejia
Optimal Control Strategies And Reinforcement Learning For Dynamical Multiagent Systems In Graphical Games, Victor Gabriel Lopez Mejia
Electrical Engineering Dissertations
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 research. Each agent in this setting is interested in completing a specific task with considerations of an optimal performance. Game theory has become one of the most useful tools in multiagent systems analysis due to its rigorous mathematical representation of optimal decision making. The analysis of dynamical systems has been developed in the branch of game theory regarded as differential games. The ser of graphical games …
Viewpoint Optimization For Autonomous Strawberry Harvesting With Deep Reinforcement Learning, Jonathon J. Sather
Viewpoint Optimization For Autonomous Strawberry Harvesting With Deep Reinforcement Learning, Jonathon J. Sather
Master's Theses
Autonomous harvesting may provide a viable solution to mounting labor pressures in the United States' strawberry industry. However, due to bottlenecks in machine perception and economic viability, a profitable and commercially adopted strawberry harvesting system remains elusive. In this research, we explore the feasibility of using deep reinforcement learning to overcome these bottlenecks and develop a practical algorithm to address the sub-objective of viewpoint optimization, or the development of a control policy to direct a camera to favorable vantage points for autonomous harvesting. We evaluate the algorithm's performance in a custom, open-source simulated environment and observe affirmative results. Our trained …
Robot Navigation In Cluttered Environments With Deep Reinforcement Learning, Ryan Weideman
Robot Navigation In Cluttered Environments With Deep Reinforcement Learning, Ryan Weideman
Master's Theses
The application of robotics in cluttered and dynamic environments provides a wealth of challenges. This thesis proposes a deep reinforcement learning based system that determines collision free navigation robot velocities directly from a sequence of depth images and a desired direction of travel. The system is designed such that a real robot could be placed in an unmapped, cluttered environment and be able to navigate in a desired direction with no prior knowledge. Deep Q-learning, coupled with the innovations of double Q-learning and dueling Q-networks, is applied. Two modifications of this architecture are presented to incorporate direction heading information that …
Optimization Of Energy Harvesting Mobile Nodes Within Scalable Converter System Based On Reinforcement Learning, Chengtao Xu
Optimization Of Energy Harvesting Mobile Nodes Within Scalable Converter System Based On Reinforcement Learning, Chengtao Xu
All Graduate Theses, Dissertations, and Other Capstone Projects
Microgrid monitoring focusing on power data, such as voltage and current, has become more significant in the development of decentralized power supply system. The power data transmission delay between distributed generator is vital for evaluating the stability and financial outcome of overall grid performance. In this thesis, both hardware and simulation has been discussed for optimizing the data packets transmission delay, energy consumption, and collision rate. To minimize the transmission delay and collision rate, state-action-reward-state-action (SARSA) and Q-learning method based on Markov decision process (MDP) model is used to search the most efficient data transmission scheme for each agent device. …
Less Is More: Beating The Market With Recurrent Reinforcement Learning, Louis Kurt Bernhard Steinmeister
Less Is More: Beating The Market With Recurrent Reinforcement Learning, Louis Kurt Bernhard Steinmeister
Masters Theses
"Multiple recurrent reinforcement learners were implemented to make trading decisions based on real and freely available macro-economic data. The learning algorithm and different reinforcement functions (the Differential Sharpe Ratio, Differential Downside Deviation Ratio and Returns) were revised and the performances were compared while transaction costs were taken into account. (This is important for practical implementations even though many publications ignore this consideration.) It was assumed that the traders make long-short decisions in the S&P500 with complementary 3-month treasury bill investments. Leveraged positions in the S&P500 were disallowed. Notably, the Differential Sharpe Ratio and the Differential Downside Deviation Ratio are risk …