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Bayiz, Y.E. (author)For single-agent problems, Reinforcement Learning (RL) algorithms proved to be useful learning optimal control laws for nonlinear dynamic systems without relying on a mathematical model of the system to be controlled. With their ability to work on continuous action and state spaces, actor-critic RL algorithms are especially advantageous in that...master thesis 2014
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Nagaki, K. (author)Reinforcement learning (RL) is a machine learning technique whereby the controller learns the control law by optimizing the received cumulative amount of reward. A reward is an instantaneous evaluation of the applied action at the current state, given by reward function. However in theory the reward function is assumed to be given, in practice...master thesis 2015
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Bhattacharjee, A. (author)The dynamics of many physical processes can be described by port-Hamiltonian (PH) models where the importance of the energy function can be seen. In Control by Interconnection (CbI), the controller is another PH system that is connected to the plant through a power preserving interconnection to add up the energy functions. However, a major issue...master thesis 2015
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Munk, J. (author)In control, the objective is to find a mapping from states to actions that steer a system to a desired reference. A controller can be designed by an engineer, typically using some model of the system or it can be learned by an algorithm. Reinforcement Learning (RL) is one such algorithm. In RL, the controller is an agent that interacts with the...master thesis 2016
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Goedhart, Menno (author)Flight control of the DelFly is challenging, because of its complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to tackle these challenges. A Policy Gradient algorithm is used to tune the gains of a Proportional-Integral controller using Reinforcement Learning. Furthermore, a novel...master thesis 2017
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Rastogi, Divyam (author)Reinforcement Learning (RL) is a general purpose framework for designing controllers for non-linear systems. It tries to learn a controller (policy) by trial and error. This makes it highly suitable for systems which are difficult to control using conventional control methodologies, such as walking robots. Traditionally, RL has only been...master thesis 2017
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Bhowal, Abhranil (author)A Reinforcement Learning (RL) agent learns about its environment through exploration. For most physical applications such as search and rescue UAVs, this exploration must take place with safety in mind. Unregulated exploration, especially at the beginning of a run, will lead to fatal situations such as crashes. One approach to mitigating these...master thesis 2017
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Ravi, Siddharth (author)This project addresses a fundamental problem faced by many reinforcement learning agents. Commonly used reinforcement learning agents can be seen to have deteriorating performances at increasing frequencies, as they are unable to correctly learn the ordering of expected returns for actions that are applied. We call this the disappearing...master thesis 2017
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Pohl, Franz (author)The Variable Camber Continuous Trailing Edge Flap (VCCTEF) is a novel aircraft control system that intents to prevent undesired aeroelastic deflections by precise lift tailoring along the wing span. However, the unknown dynamics and increased complexity of the new hardware imposes difficulties to establish an optimal controller. One approach is...master thesis 2017
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Leest, Steven (author)Robotic behavior policies learned in simulation suffer from a performance degradation once transferred to a real-world robotic platform. This performance degradation originates from discrepancies between the real-world and simulation environment, referred to as the reality gap. To cross the reality gap, this papers presents a simple...master thesis 2017
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Vermeer, Kaz (author)Advanced tools such as machine learning are slowly finding their way into the modern scientist’s toolbox . In the design of mechanical systems however hardly any machine learning applications are being used. Research into the viability of such an application is therefore necessary.<br/>We have performed such research, using a specific type of...master thesis 2017
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Keulen, Bart (author)An important problem in reinforcement learning is the exploration-exploitation dilemma. Especially for environments with sparse or misleading rewards it has proven difficult to construct a good exploration strategy. For discrete domains good exploration strategies have been devised, but are often nontrivial to implement on more complex domains...master thesis 2018
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Siddiquee, Manan (author)Reinforcement Learning (RL) has been applied to teach quadcopters guidance tasks. Most applications rely on position information from an absolute reference<br/>system such as Global Positioning System (GPS). The dependence on "absolute<br/>position" information is a general limitation in the autonomous flight of Unmanned Aerial Vehicles (UAVs)....master thesis 2018
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Juchli, Marc (author)For various reasons, financial institutions often make use of high-level trading strategies when buying and selling assets. Many individuals, irrespective or their level of prior trading knowledge, have recently entered the field of trading due to the increasing popularity of cryptocurrencies, which offer a low entry barrier for trading....master thesis 2018
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Cherici, Teo (author)Recent advancements in computation power and artificial intelligence have allowed the creation of advanced reinforcement learning models which could revolutionize, between others, the field of robotics. As model and environment complexity increase, however, training solely through the feedback of environment reward becomes more difficult. From...master thesis 2018
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Starre, Rolf (author)Recent Reinforcement Learning methods have combined function approximation and Monte Carlo Tree Search and are able to learn by self-play up to a very high level in several games such as Go and Hex. One aspect in this combination<br/>that has not had a lot of attention is the action selection policy during self-play, which could influence the...master thesis 2018
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Sawant, Shambhuraj (author)Reinforcement learning (RL) is an area of Machine Learning (ML) concerned with learning how a software-defined agent should act in an environment to maximize the rewards. Similar to many ML methods, RL suffers from the curse of dimensionality, the exponential increase in solution space with the increase in problem dimensions. Learning the...master thesis 2018
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Khattar, Varun (author)Closed-loop control systems, which utilize output signals for feedback to generate control inputs, can achieve high performance. However, robustness of feedback control loops can be lost if system changes and uncertainties are too large. Adaptive control combines the traditional feedback structure with providing adaptation mechanisms that adjust...master thesis 2018
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Ashraf, Imrul (author)Loss of control-in flight (LOC-I) is one of the causes of catastrophic aircraft accidents. Fault-tolerant flight control (FTFC) systems can prevent LOC-I and recover aircraft from its precursors. One group of promising methods for developing Fault-Tolerant Control (FTC) system is the Adaptive Critic Designs (ACD). Recently one ACD algorithm,...master thesis 2018
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van Hoorn, Martijn (author)To increase performance of air-to-air missile guidance, a novel guidance law is developed using reinforcement learning methods. This guidance law is based on behavior obtained from optimal control methods and subsequently aims to approximate its performance. The study compares the developed guidance law to a traditional guidance law and optimal...master thesis 2019