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 20 of 1006 for “"Reinforcement Learning"”.
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Feature reinforcement learning agents
Reinforcement Learning (RL) is currently an active research area of Artificial Intelligence (AI) in which an agent interacts with an unknown environment in order to collect as much reward as possible. One of the most challenging problems in AI is the General Reinforcement Learning (GRL) problem …
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Feature reinforcement learning agents
Reinforcement Learning (RL) is currently an active research area of Artificial Intelligence (AI) in which an agent interacts with an unknown environment in order to collect as much reward as possible. One of the most challenging problems in AI is the General Reinforcement Learning (GRL) problem …
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Sample-efficient reinforcement learning
Reinforcement learning has been instrumental in the recent advances made by artificial intelligence agents in various domains. Most of these advances have been abetted by the availability of huge amounts of training data. But, in several practical applications such as those arising in wireless …
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Data Efficient Reinforcement Learning
Reinforcement learning (RL) has recently emerged as a generic yet powerful solution for learning complex decision-making policies, providing the key foundational underpinnings of recent successes in various domains, such as game playing and robotics. However, many state-of-the-art algorithms are …
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An Introduction to Reinforcement Learning
This thesis presents a new course textbook on reinforcement learning (RL) with a focus on algorithms and their properties. The textbook is suitable for a one-semester introductory undergraduate course on RL for students with prior experience in basic probability, linear algebra, and multivariable …
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Differential Privacy in Reinforcement Learning
Reinforcement learning is a principled AI framework for autonomously experience-driven learning. The primary goal of reinforcement learning is to train autonomous agents to learn the optimal behaviors for their interactive environments. Deep reinforcement learning promotes a higher-level …
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Deep reinforcement learning for quadrupeds
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms
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On Zero-Shot Reinforcement Learning
Modern reinforcement learning (RL) systems capture deep truths about general, human problem-solving. In domains where new data can be simulated cheaply, these systems uncover sequential decision-making policies that far exceed the ability of any human. Society faces many problems whose solutions …
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Reinforcement learning for telescope optimisation
Reinforcement learning is a relatively new and unexplored branch of machine learning with a wide variety of applications. This study investigates reinforcement learning and provides an overview of its application to a variety of different problems. We then explore the possible use of reinforcement …
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Generative Discovery via Reinforcement Learning
… to improve existing solutions (like learning more efficient ways to walk or synthesizing novel compounds)? Can we design computational models that mimic or exceed human discovery? Such computational models could greatly accelerate progress in science and engineering since they can …
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Reinforcement learning in network control
… often unknown, and need to be learned. Existing reinforcement learning methods such as Q-Learning, Actor-Critic, etc. are heuristic and do not offer performance guarantees. In contrast, model-based learning methods offer performance guarantees, but can only be applied with bounded state spaces. …
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Efficient Reinforcement Learning for Control
… has evolved rapidly with the emergence of Reinforcement Learning (RL), offering promising solutions to a wide range of dynamic decision-making problems. However, the application of RL to real-world control systems is often hindered by computational inefficiencies, scalability issues, and a …
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Market Timing strategy through Reinforcement Learning
… an optimal trading strategy based on the machine learning method and extreme value theory (EVT) to obtain an excess return on investments in the capital market. The trading strategy outperforms the benchmark S&P 500 index with higher returns and lower volatility through effective market timing. In …
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Reinforcement Learning of Distributed Surveillance Plans
… describes the design and implementation of a Reinforcement Learning algorithm on a camera surveillance model which is used to know the stackelberg strategies of attacker and defender. This reinforcement learning algorithm is compared with the uniform policy and hill climbing algorithms by …
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TCP congestion control using reinforcement learning
… in this space was relatively limited. Recently, Reinforcement Learning (RL), a form of AI, has been explored in the networking space, and in enhancing the performance of TCP, this Thesis aims to expand the use of RL for TCP (TCP-CA/RL) in a software-defined data center. We demonstrate that our …
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Probabilistic Ecosystems Assessment with Reinforcement Learning.
… probabilistic ecosystems assessment and reinforcement learning (RL) to develop adaptive, explainable tools for biodiversity monitoring in the context of autonomous underwater vehicles (AUVs). At the core of PEARL lies HexaWorld, a reinforcement learning environment structured on hexagonal …
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