- document
-
Koning, Tim (author)Reinforcement Learning (RL) is a learning paradigm where an agent learns a task by trial and error. The agent needs to explore its environment and by simultaneously receiving rewards it learns what is appropriate behaviour.<br/>Even though it has roots in machine learning, RL is essentially different from other machine learning methods. In...master thesis 2020
- document
-
Gupta, Sukrit (author)A lot research has been conducted in the field of autonomous navigation of mobile robots with focus on Robot Vision and Robot Motion Planning. However, most of the classical navigation solutions require several steps of data pre-processing and hand tuning of parameters, with separate modules for vision, localization, planning and control. All...master thesis 2020
- document
-
Konatala, Ramesh (author)Online Adaptive Flight Control is interesting in the context of growing complexity of aircraft systems and their adaptability requirements to ensure safety. An Incremental Approximate Dynamic Programming (iADP) controller combines reinforcement learning methods, optimal control and Online identified incremental model to achieve optimal adaptive...master thesis 2020
- document
-
Scavuzzo Montana, Lara (author)Mixed Integer Linear Programming (MILP) is a generalization of classical linear programming where we restrict some (or all) variables to take integer values. Numerous real-world problems can be modeled as MILPs, such as production planning, scheduling, network design optimization and many more. MILPs are, in fact, NP-hard. State-of-the-art...master thesis 2020
- document
-
Monteiro Nunes, Tiago (author)Reinforcement Learning (RL) focuses on maximizing the returns (discounted rewards) throughout the episodes, one of the main challenges when using it is that it is inadequate for safety-critical tasks due to the possibility of transitioning into critical states while exploring. Safe Reinforcement Learning (SafeRL) is a subset of RL that focuses...master thesis 2019
- document
-
Lee, Jun (author)Reinforcement learning is used as a type of adaptive flight control. Adaptive Critic Design (ACD) is a popular approach for online reinforcement learning control due to its explicit generalization of the policy evaluation and the policy improvement elements. A variant of ACD, Incremental Dual Heuristic Programming (IDHP) has previously been...master thesis 2019
- document
-
Hoogvliet, Jonathan (author)Reinforcement learning (RL) is a model-free adaptive approach to learn a non-linear control law for flight control. However, for flat-RL (FRL) the size of the search space grows exponentially with the number of states, resulting in low sample efficiency. This research aims to improve the efficiency with Hierarchical Reinforcement Learning (HRL)....master thesis 2019
- document
-
Tian, Yuan (author)Model-free reinforcement learning has proved to be successful in many tasks such as robotic manipulator, video games, and even stock trading. However, as the dynamics of the environment is unmodelled, it is fundamentally difficult to ensure the learned policy to be absolutely reliable and its performance is guaranteed. In this thesis, we borrow...master thesis 2019
- document
-
Valentini, Carlo (author)Machine Learning Control is a control paradigm that applies Artificial Intelligence methods to control problems. Within this domain, the field of Reinforcement Learning (RL) is particularly promising, since it provides a framework in which a control policy does not have to be programmed explicitly, but can be learned by an intelligent controller...master thesis 2019
- document
-
Kranen, Tommy (author)With all major bodies within the Solar System explored by at least a single fly-by, modern-day missions are becoming increasingly more demanding, up to a point where classical chemical propulsion can no longer supply the required ∆V. Increasingly more is relied upon low-thrust propulsion, characterised by its (very) low thrust force; long...master thesis 2019
- document
-
Arnaoutis, Vasos (author)Deep Learning performance dependents on the application and methodology. Neural Networks with convolutional layers have been a great success in multiple tasks trained under Supervised Learning algorithms. For higher dimensional problems, the selection of a deep network architecture can significantly improve the accuracy of the network, however...master thesis 2019
- document
-
van Dam, Geart (author)This research investigates and proposes a new method for obstacle detection and avoidance on quadrotors. One that does not require the addition of any sensors, but relies solely on measurements from the accelerometer and rotor controllers. The detection of obstacles is based on the principle that the airflow around a quadrotor changes when the...master thesis 2019
- document
-
Vonk, Bart (author)Research on reinforcement learning algorithms to play complex video games have brought forth controllers surpassing human performance. This paper explores the possibilities of applying these techniques to the sequencing and spacing of aircraft. Two experiments are performed. First a single aircraft must learn to fly a 4D trajectory using only...master thesis 2019
- document
-
Scholten, Jan (author)Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems. Modelling and control design is longer required, which paves the way to numerous in- novations, such as optimal control of evermore sophisticated robotic systems, fast and efficient scheduling and logistics, effective personal drug dosing...master thesis 2019
- document
-
Heyer, Stefan (author)In recent years Adaptive Critic Designs (ACDs) have been applied to adaptive flight control of uncertain, nonlinear systems. However, these algorithms often rely on representative models as they require an offline training stage. Therefore, they have limited applicability to a system for which no accurate system model is available, nor readily...master thesis 2019
- document
-
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
- document
-
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
- document
-
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
- document
-
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
- document
-
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