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.
Results
Showing 1 to 20 of 21 for “"Deep Q Learning"”.
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Automated Ligand Design in Simulated Molecular Docking - Optimising ligand binding affinity through the application of deep Q-learning to docking simulations
… for accelerating this process with machine learning algorithms that can automatically design novel ligands for biological targets. Recent work has demonstrated the viability of deep reinforcement learning, generative adversarial networks and auto-encoders. Here, we extend state-of-the-art …
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Graph matching by graph neural network
… two correlated graphs. This thesis presents a deep Q learning based method, which represents the matching process by a graph neural network. By breaking the symmetry, the parameterized graph neural network is able to capture a wide range of neighborhoods. Extensive experiments on various …
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Reinforcement learning with natural language signals
… Language as part of the state in Reinforcement Learning. We show that it is capable of solving Natural Language problems, similar to Sequence-to-Sequence models, but using multistage reasoning. We use Long Short-Term Memory Networks to parse the Natural Language input, whose final hidden state …
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Efficient reinforcement learning through variance reduction and trajectory synthesis
Reinforcement learning is a general and unified framework that has been proven promising for many important AI applications, such as robotics, self-driving vehicles. However, current reinforcement learning algorithms suffer from large variance and sampling inefficiency, which leads to slow …
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Creation of a Cognitive Radar with Machine Learning: Simulation and Implementation
… a Markov Decision Process (MDP), and then apply Deep-Q Learning to optimize radar performance. The radar environment includes a single point target and a communications system that will potentially interfere with the radar. We demonstrate that the Deep-Q Network (DQN) we construct is able to …
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Robot Navigation in Cluttered Environments with Deep Reinforcement Learning
… 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 …
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Application of genetic algorithm and deep reinforcement learning for in-core fuel management
… process are explored. Genetic algorithm and deep Q-learning are applied in an attempt to reduce core design time and improve the final core layout. The reference core represents a 4-loop pressurized water reactor where fixed number of fuel enrichments and burnable poison distributions are …
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Otimização de semáforos utilizando aprendizado por reforço com redes neurais
… com Redes Neurais por meio da técnica de Deep Q-Learning para aprender a prever, de forma eficaz, uma política que otimize um semáforo em um cruzamento específico no bairro do Ingá, na cidade de Niterói. Com a implementação da técnica mencionada e usando ferramentas de simulação foi …
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Zero-shot learning to execute tasks with robots
… the art in robotic planning with reinforcement learning. We are interested in designing a generalizable framework with several features, namely: allowing for zero-shot learning agents that are robust and resilient in the event of failing midway during a task, allowing us to detect failures, and …
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ACADIA: Efficient and Robust Adversarial Attacks Against Deep Reinforcement Learning
Existing adversarial algorithms for Deep Reinforcement Learning (DRL) have largely focused on identifying an optimal time to attack a DRL agent. However, little work has been explored in injecting efficient adversarial perturbations in DRL environments. We propose a suite of novel DRL adversarial …
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Cognitive GPR for subsurface sensing based on edge computing and deep reinforcement learning
… GPR based on 2D B-Scan image analysis and deep Q-learning network (DQN) is investigated. A novel entropy-based reward function is designed for the DQN model by using the results of subsurface object detection (via the region of interest identification) and recognition (via classification). …
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Congestion Control for V2V Communication in VANET
… both traditional and innovative machine learning-based approaches, with the goal of improving communication efficiency while maintaining vehicle awareness. Traditional congestion control methods, such as rate-based and power-based approaches, primarily focus on optimizing transmission …
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Data-driven adaptive learning systems
"Adaptive learning systems are capable of providing more adaptive and efficient assessment and learning experiences for learners than traditional classroom settings. A conventional adaptive learning system involves a learner, a latent trait estimator, and a learning strategy/plan. The latent trait …
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Trajectory Generation for a Multibody Robotic System: Modern Methods Based on Product of Exponentials
… trajectories which are generated using Machine Learning (ML) and Artificial Neural Networks (ANNs) techniques. The CSA method smooths the trajectory in the Special Euclidean (SE(3)) space. In the third approach, a multi-objective Swarm Intelligence (SI) trajectory generation algorithm is …
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Optimizing Verification of RTL Designs Using Reinforcement Learning Methods
… by exploring the use and the integration of deep reinforcement learning techniques in a CFV environment. The research makes use of a deep learning approach to a Q-Learning variant, the same approach as used in the work by DeepMind Technologies to play Atari games. The RTL design used …
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Multi-type Fair Resource Allocation for Distributed Multi-Robot Systems
… robots to accomplish the task. We leverage the Deep Q-learning Network (DQN) to support requester selection. The results suggest that the DQN outperforms the commonly used Q-learning.</p> <p>Finally, we propose two decentralized solutions to promote fair resource allocation in MTR-SRT, as a …
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Dynamic Reinforcement Learning-based Resource Allocation For Grant-Free Access
… thesis proposes an intelligent Reinforcement Learning (RL) based allocation technique for GF access that is trained via Deep Q-Learning. RL has the capability to learn the intricacies of the network and the behavior of the connected users to optimize resource allocation without the need for …
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Trust-aware virtual network embedding algorithms for wireless sensor networks
… objective, we develop a novel reinforcement learning-based (RL-based) trust-aware virtual wireless sensor network (RLT-VWSN) algorithm by employing a policy network and extracting attributes of the substrate nodes to get the mapping probability of each one. This algorithm is superior to …
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Smart Process Design with Machine Learning for Quality Assurance in Metal Additive Manufacturing
… parameter optimization, (2) reinforcement learning-enabled scan path planning, and (3) a multi-fidelity Bayesian optimization framework for efficient process parameter tuning. Collectively, these approaches enhance process control and reduce defects, advancing LPBF toward greater …
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Deep reinforcement learning for adaptive monitarizacion and patrolling of water resources with unmanned surface vehicles
… single-agent and multiagent perspective using Deep Reinforcement Learning to deal with high computational complexity and dimensionality. These techniques allow the use of Neural Networks to estimate a policy capable of acting by maximizing a defined reward function for such tasks. Specifically, …
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