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 376 for “"RL"”.
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RL-SAR: A Robotic System for Fine-Grained RFID Localization using RL-based Synthetic Aperture Radar
… precise and efficient localization. We introduce RL-SAR, an end-to-end autonomous Synthetic Aperture Radar (SAR) based RFID localization system, utilizing a Reinforcement Learning (RL) algorithm to determine the most optimal trajectory for localizing multiple tags. We implemented this system with …
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Transfer Function Synthesis Based on Cascaded Rc and Rl Networks
Made available in DSpace on 2014-12-04T21:03:18Z (GMT). No. of bitstreams: 1 6100095.pdf: 4081980 bytes, checksum: ba36a9468461154c544803ca924a0055 (MD5) Previous issue date: 1960
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AC-RL: A Framework for Real-Time Control, Learning & Adaptation
… in the inner loop and a Reinforcement Learning (RL) based policy in the outer loop is proposed such that in real-time the inner-loop model reference adaptive controller contracts the closed-loop dynamics towards a reference system, while the RL in the outerloop directs the overall system towards …
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Implementing a hazard elimination analysis tool for SpecTRM-RL using backwards reachability
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
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The Finite Embeddability Property for Some Noncommutative Knotted Varieties of RL and DRL
… general case, the free object in the class is fairly complicated, so we identify instead an object outside the class, which is both free and structured enough to allow us to prove the result. In the last section, we extend our result to cover some other subvarieties of knotted residuated lattices. …
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Addressing deep reinforcement learning: empirical algorithm performance evaluations∗
… paced production of deep reinforcement learning (RL) research papers, some recent publications have begun to critique the manner in which RL algorithm performances are evaluated. Building on this recent scrutiny, our work attempts to identify the precise aspects of empirical deep RL algorithm …
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Nonlinear adaptation of established linear and predictive control laws through safe reinforcement learning
Reinforcement learning (RL) enables the prospect of data-driven controllers that learn to select control actions optimally purely through the feedback provided by an evaluative signal (the reward). In principle, this technology may be used to develop adaptive controllers that account for …
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Die Infrarot-Radiolumineszenz von Feldspäten und ihr Einsatz in der Lumineszenzdatierung
Die vorliegende Arbeit beschäftigt sich mit der Methodik und Anwendung der Radiolumineszenz-Datierung an Feldspäten. Der Schwerpunkt liegt auf der infraroten Radiolumineszenzemission (IR-RL) von Feldspäten. Um Messungen der IR-RL durchführen zu können, wurden geeignete Messgeräte entwickelt und …
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Forecasting product returns and their impact on dynamic performance in closed-loop supply chains
… manufacturers are introducing reverse logistics (RL) into their forward supply chain (SC). RL together with forward logistics consists of a closed-loop supply chain (CLSC). This research aims to establish a systematic understanding of RL systems with a focus on product returns and their impacts on …
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Responsible leadership competencies in accounting education
The business world continues to be plagued by incidents of leadership failure, including that of Chartered Accountants (CAs). Responsible Leadership (RL) theory was in part conceived in response to misconduct by business leaders. RL competencies overlap with many competencies expected of CAs, …
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Inductive Biases in Learning Hierarchical Abstractions for Bipedal Locomotion
… in the field of reinforcement learning (RL), due to the high dimensional state and action space. Hierarchical abstractions and inductive biases emerge as critical components in navigating this complexity, offering pathways for effective learning and adaptation in bipedal locomotion tasks. …
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Designing policy optimization algorithms for multi-agent reinforcement learning
Multi-agent reinforcement learning (RL) studies the sequential decision-making problem in the setting where multiple agents exist in an environment and jointly determine the environment transition. The relationship between the agents can be cooperative, competitive, or mixed depending on how the …
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Investigation on reverse logistics of end of life cars in the UK
… becoming the most significant problem in the world, which generally attributed to the greenhouse effect caused by increased levels of carbon dioxide, CFCs, and other pollutants. This has forced government and business to focus on environmental issues on their initiatives where reverse logistics …
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Benchmarking Reinforcement Learning and Off Policy Evaluation for Medical Decision Making
… challenges to existing Reinforcement Learning (RL) methods due to implementation risks, low data availability, short treatment episodes, sparse re[1]wards, partial observations, and heterogeneous treatment effects (HTE). Despite significant interest in developing Dynamic Treatment Regimes (DTRs) …
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An Introduction to Reinforcement Learning
… 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 calculus. We …
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The Limits of Temporal Abstractions for Reinforcement Learning with Sparse Rewards
… are intended to improve reinforcement learning (RL) performance through hierarchical RL. Despite our intuition about the properties of an environment that make skills useful, there has been little theoretical work aimed to characterize these properties precisely. This work studies the utility of …
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Reinforcement Learning assisted Adaptive difficulty of Proof of Work (PoW) in Blockchain-enabled Federated Learning
… of heterogeneity in blockchain mining, particularly in the context of consortium and private blockchains. The motivation stems from ensuring fairness and efficiency in blockchain technology's Proof of Work (PoW) consensus mechanism. Existing consensus algorithms, such as PoW, PoS, and PoB, have …
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Reinforcement Actor-Critic Learning As A Rehearsal In MicroRTS
… successes based on deep reinforcement learning (RL). However, RL remains a data-hungry approach featuring a high sample complexity. In this thesis, we focus on a sample complexity reduction technique called reinforcement learning as a rehearsal (RLaR), and on the RTS game of MicroRTS to formulate …
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SAFE REINFORCEMENT LEARNING-BASED GREEN LIGHT OPTIMAL SPEED ADVISORY FOR MIXED-TRAFFIC PLATOONS
This thesis develops a platoon-centric, safe RL-based Green Light Optimal Speed Advisory (GLOSA) system to optimize the CAV speed profile of a mixed-traffic platoon. First, we design a multi-agent RL algorithm to achieve a balance between the energy and travel efficiency of a mixed-traffic platoon, …
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Towards Reinforcement-Learning-based Robot Navigation with 3D Scene Graphs
Applying Reinforcement Learning (RL) for autonomous navigation has enormous potential in several robotics applications, including search and rescue operations. RL circumvents the need to manually specify a control policy for navigation and allows capturing aspects that are difficult to describe …
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