Search Results - "Reinforcement learning"

Human-in-the-loop reinforcement learning enables non-expert users to train agents through interactive feedback. While preference-based methods (PbRL) have gained popularity by allowing humans to express preferences between trajectory pairs, they are limited by their narrow feedback interface - human...

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Determining the best medium pulse repetition frequency (PRF) set for radar has long been a problem. Because it cannot be represented in an analytical form, it is not possible to solve it using conventional optimization methods. Although heuristic methods can provide a good PRF set, determining the b...

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Efficient learning requires estimation of, and adaptation to, different forms of uncertainty. If uncertainty is caused by randomness in outcomes (noise), observed events should have less influence on beliefs, whereas if uncertainty is caused by a change in the process being estimated (volatility) th...

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The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep rein...

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This study proposes a robust methodology for vibration suppression and trajectory tracking in rotary flexible-link systems by leveraging guided reinforcement learning (GRL). The approach integrates the twin delayed deep deterministic policy gradient (TD3) algorithm with a linear quadratic regulator...

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Carbon neutrality, as a fundamental goal of global sustainability, is constrained by the carbon lock-in effect, which limits the progress of low-carbon transitions. Ecological compensation mechanisms offer a promising solution by optimizing land use and enhancing carbon sequestration capacity. Howev...

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The Minority Game (MG) is a paradigmatic model in econophysics, widely used to study inductive reasoning and self-organization in multi-agent systems. Traditionally, coordinated phases in the MG are associated with spontaneous symmetry breaking, where agents differentiate into polarized roles. Recen...

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In this paper, we propose a novel channel estimation approach and driving decision method for adaptive cruise control (ACC) systems for vehicular networks, leveraging the properties of deep learning, reinforcement learning, and orthogonal time frequency space (OTFS) modulation. To achieve that, we p...

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The evolution of blockchain technology across various areas has highlighted the importance of optimizing blockchain systems' performance, especially in fluctuating network bandwidth conditions. We observe that the performance of blockchain systems exhibits variations, and the optimal parameter...

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In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to identify valid input spaces in high-dimensional, highly constrained systems while minimizing the total runtime o...

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This exploratory study proposes an investigation into AI-generated productions compared to L2 student-written productions within the context of English Language Learning. Initiated within a broader pedagogical shift from handwritten to typed productions, and coinciding with the rise of AI tools suc...

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Trajectory planning is crucial for solar aircraft endurance. The multi-wing morphing solar aircraft can enhance solar energy acquisition through wing deflection, which simultaneously incurs aerodynamic losses, complicating energy coupling and challenging existing planning methods in efficiency and l...

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Advancements in bioelectronic sensors and actuators have paved the way for real-time monitoring and control of the progression of wound healing. Real-time monitoring allows for precise adjustment of treatment strategies to align them with an individual’s unique biological response. However, due to t...

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With the rapid development of mobile internet technology and increasing concerns over data privacy, Federated Learning (FL) has emerged as a significant framework for training machine learning models. Given the advancements in technology, User Equipment (UE) can now process multiple computing tasks...

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In federated learning, selecting participating devices (clients) is critical due to their inherent diversity. Clients typically hold non-IID data and possess varying computational and communication capabilities, which introduces heterogeneity that can impact overall system performance. Ignoring this...

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With the advance of Artificial Intelligence, Deep Neural Networks are widely employed in various sensor-based systems to analyze operational conditions. However, due to the inherently nondeterministic and probabilistic natures of neural networks, the assurance of overall system performance could bec...

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Reinforcement Learning (RL) enables agents to learn optimal policies through environment interaction, with Q-learning being a fundamental algorithm for Markov Decision Processes (MDPs). However, Q-learning suffers from slow convergence due to its exhaustive exploration requirements, particularly in...

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As deep learning and reinforcement learning technologies advance, intelligent rural information management is transforming substantially. This article presents an innovative framework, the evolutionary bidirectional long short-term memory deep Q-network (EBLM-DQN), which integrates evolutionary algo...

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We propose herein <b>LLM-Guided Reinforcement Learning (LGRL)</b>, a novel framework that leverages large language models (LLMs) to decompose high-level objectives into a sequence of manageable subgoals in interactive environments. Our approach decouples high-level planning from low-leve...

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Loading and unloading rolling cargo in roll-on/roll-off are important and very recurrent operations in maritime logistics. In this paper, we apply state-of-the-art deep reinforcement learning algorithms to automate these operations in a complex and real environment. The objective is to teach an auto...

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