Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 9 of 9 for “"Deep Q Networks"”.
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Enhancing Molecular Docking with Deep Q-Networks
… aprovechar los prometedores algoritmos de Deep RL para mejorar la resolución del problema de Docking. Para ello, el hilo conductor de esta tesis doctoral son las diferentes alternativas de representación de las moléculas de la escena de Docking que serán utilizadas como datos de entrada de …
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Training and Inference in Early-Exit Deep Q-Networks for Efficient Reinforcement Learning
Neural networks have become instrumental in various machine learning tasks, but their increasing complexity poses challenges in terms of computational resources and real-time decision-making. To address these challenges, this thesis explores the integration of early-exit neural networks (EENNs) …
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Market making in dry waters : reinforcement learning strategies for market making in illiquid markets
… The study implements three RL algorithms: Deep Q-Networks (DQN), Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO). They are evaluated in a simulated stock market environment on their performance in liquid and illiquid market conditions. Findings show that DQN outperforms …
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Hybrid AI-driven Approach to Context-Aware Inter-Slice Load Balancing for Cloud-Native Functions in 5G Networks
… for intelligent capabilities. Graph neural networks (GNN) and spatio-temporal multi-head graph attention networks (SP-mGAT) are utilized to generate context-aware embeddings, clustering clients by traffic characteristics into priority labels. These labels feed multi-agent deep reinforcement …
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Optimizing Routing Strategy in Software Defined Networking
… The project employs a combination of Dueling Deep Q-Networks (Dueling DQN) and real-time traffic state predictions to create a dynamic routing strategy. The methodology includes extensive simulation using SDN environments to evaluate the performance improvements over traditional routing …
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Zero-shot learning to execute tasks with robots
… For this, we first explore the use of deep Q networks to control a robot. Upon finding deep Q learning too unstable, we determine that Q networks alone are insufficient for attaining true resilience. Second, we explore the use of more powerful actor-critic methods, augmented with …
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Deep Reinforcement Learning for Next Generation Wireless Networks with Echo State Networks
This dissertation considers a deep reinforcement learning (DRL) setting under the practical challenges of real-world wireless communication systems. The non-stationary and partially observable wireless environments make the learning and the convergence of the DRL agent challenging. One way to …
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Machine Learning Enhanced Power Converter Design and Prognostic Health Monitoring in DC Distribution Systems
… dominance to emerging methods like Graph Neural Networks (GNNs) and Generative Models (GenAI), and pinpointing open research gaps. Chapter 2 tackles the slow, manual converter design workflow by introducing a computer vision and OCR-based digitize-and-simulate pipeline that converts electrical …
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Single-player to Two-player Knowledge Transfer in Atari 2600 Games
Playing two-player games using reinforcement learning and self-play can be challenging due to the complexity of two-player environments and the potential instability in the training process. It is proposed that a reinforcement learning algorithm can train more efficiently and achieve improved …