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In this master thesis project a LiDAR-based, depth image-based and semantic segmentation image-based reinforcement learning agent is investigated and compared forlearning in simulation and performing in real-time. The project utilize the Deep Deterministic Policy Gradient architecture for learning continuous actions and was designed to control a RC car. One of the first project to deploy an agent in a real scenario after training in a similar simulation. The project demonstrated that with a proper reward function and by tuning driving parameters such as restricting steering, maximum velocity, minimum velocity and performing input data scaling a LiDAR-based agent could drive indefinitely on a simple but completely unseen track in real-time.

Die Arbeit ist Teil eines aktuellen Forschungsprojekts, bei der ein dynamischer zweidimensionaler Verkehrsflusssimulator zur Beschreibung der Binnenschifffahrt auf einer ca. 220 km langen Strecke auf dem Niederrhein entwickelt werden soll. Ziel dieser Arbeit ist es, ein Schifffolgemodell mithilfe von Deep Learning Ansätzen umzusetzen und mittels geeigneter Beschleunigung ein kollisionsfreies Folgen zu realisieren. Dabei sind die gesetzlichen Randbedingungen (Verkehrsregeln, Mindestabstände) sowie hydrodynamische und physikalische Gesetzmäßigkeiten wie minimale und maximale Beschleunigungen und Geschwindigkeiten zu berücksichtigen. Nach der Analyse des Systems sowie der notwendigen Parameter, wird ein Modell entworfen und die Modellparameter bestimmt. Unter Berücksichtigung der Modellparameter wird ein Agent ausgewählt und das System in MATLAB implementiert. Die Parameter sind so gestaltet, dass sich damit ein allgemeines Folgemodell ergibt und beispielsweise auch ein Autofolgemodell realisieren lässt.:1 Einleitung 1.1 Ziel der Arbeit 1.2 Aufbau der Arbeit 2 Stand der Technik 2.1 Traditionelle Folgemodelle 2.2 Reinforcement Learning 2.2.1 Modell 2.2.2 State-value function 2.3 Deep Reinforcement Learning 2.3.1 Künstliches neuronales Netz 3 Mathematische Grundlagen 3.1 Künstliche Neuronen 3.1.1 Aktivierungsfunktionen 3.2 Normierung 3.3 Funktionstypen 4 Analyse 4.1 Analyse der Systemfunktionen der Software 5 Modell 5.1 Aufbau 5.2 Approximatoren 5.3 Parameter 5.4 Szenarien 6 Agent 6.1 Auswahl des Agenten 6.2 Twin-Delayed Deterministic Policy Gradient (TD3) 7 Implementierung 7.1 Environment 7.1.1 Rewardfunktion 7.2 Agent 7.2.1 Netzwerkarchitektur 7.2.1.1 Actor-Netzwerk 7.2.1.2 Critic-Netzwerk 7.2.1.3 Rauschprozesse 7.3 Hyperparameter 7.4 Sonstige Parameter 8 Trainingsprozess 45 8.1 Ornstein-Uhlenbeck-Prozess 8.2 Algorithmus 9 Validierung 9.1 Fahrverhalten bei verschiedenen Charakteristika 9.2 Vergleich mit dem Intelligent Driver Model 10 Zusammenfassung und Ausblick Literaturverzeichnis

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The building sector is the single largest contributor to greenhouse gas emissions, making it a natural focal point for reducing energy consumption. More efficient use of energy is also becoming increasingly important for property managers as global energy prices are skyrocketing. This report is conducted on behalf of Sustainable Intelligence, a Swedish company that specializes in building automation solutions. It investigates whether deep reinforcement learning (DLR) algorithms can be implemented in a building control environment, if it can be more effective than traditional solutions, and if it can be achieved in reasonable time. The algorithms that were tested were Deep Deterministic Policy Gradient, DDPG, and Proximal Policy Optimization, PPO. They were implemented in a simulated BOPTEST environment in Brussels, Belgium, along with a traditional heating curve and a PI-controller for benchmarks. DDPG never converged, but PPO managed to reduce energy consumption compared to the best benchmark, while only having slightly worse thermal discomfort. The results indicate that DRL algorithms can be implemented in a building environment and reduce green house gas emissions in a reasonable training time. This might especially be interesting in a complex building where DRL can adapt and scale better than traditional solutions. Further research along with implementations on physical buildings need to be done in order to determine if DRL is the superior option.

<p> Within recent years, there has been a renewed interest in developing data-driven learning based algorithms for solving longstanding challenging control problems. This interest is primarily motivated by the availability of ubiquitous data and an increase in computational resources of modern machines.  However, there is a prevailing concern on the lack of provable performance guarantees on data-driven/model-free learning based control algorithms. This dissertation focuses the following key aspects: i) with what facility can state-of-the-art learning-based control methods eke out successful performance for challenging flight control applications such as aerobatic maneuvering?; and ii) can we leverage well-established tools and techniques in control theory to provide some provable guarantees for different types of learning-based algorithms?  </p> <p>To these ends, a deep RL-based controller is implemented, via high-fidelity simulations, for Fixed-Wing aerobatic maneuvering. which shows the facility with which learning-control methods can eke out successful performances and further encourages the development of learning-based control algorithms with an eye towards providing provable guarantees.<br> </p> <p>Two learning-based algorithms are also developed: i) a model-free algorithm which learns a stabilizing optimal control policy for the bilinear biquadratic regulator (BBR) which solves the regulator problem with a biquadratic performance index given an unknown bilinear system; and ii) a model-free inverse reinforcement learning algorithm, called the Model-Free Stochastic inverse LQR (iLQR) algorithm, which solves a well-posed semidefinite programming optimization problem to obtain unique solutions on the linear control gain and the parameters of the quadratic performance index given zero-mean noisy optimal trajectories generated by a linear time-invariant dynamical system. Theoretical analysis and numerical results are provided to validate the effectiveness of all proposed algorithms.</p>

Humans have a tendency to impute structure spontaneously even in simple learning tasks, however the way they approach structure learning can vary drastically. The present study sought to determine why individuals learn structure differently. One hypothesized explanation for differences in structure learning is individual differences in cognitive control. Cognitive control allows individuals to maintain representations of a task and may interact with reinforcement learning systems. It was expected that individual differences in propensity to apply cognitive control, which shares component processes with hierarchical reinforcement learning, may explain how individuals learn structure differently in a simple structure learning task. Results showed that proactive control and model-based control explained differences in the rate at which individuals applied structure learning.

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<p dir="ltr">The concept of reinforcement learning has become increasingly relevant in learning- based applications, especially in the field of autonomous navigation, because of its fundamental nature to operate without the necessity of labeled data. However, the infeasibility of training reinforcement learning based autonomous navigation applications in a real-world setting has increased the popularity of researching and developing on autonomous navigation systems by creating simulated environments in game engine platforms. This thesis investigates the comparative performance of Unity and Unreal Engine 5 within the framework of a reinforcement learning system applied to autonomous race car navigation. A rudimentary simulated setting featuring a model car navigating a racetrack is developed, ensuring uniformity in environmental aspects across both Unity and Unreal Engine 5. The research employs reinforcement learning with genetic algorithms to instruct the model car in race track navigation; while the tools and programming methods for implementing reinforcement learning vary between the platforms, the fundamental concept of reinforcement learning via genetic algorithms remains consistent to facilitate meaningful comparisons. The implementation includes logging of key performance variables during run times on each platform. A comparative analysis of the performance data collected demonstrates Unreal Engine's superior performance across the collected variables. These findings contribute insights to the field of autonomous navigation systems development and reinforce the significance of choosing an optimal underlying simulation platform for reinforcement learning applications.</p>