In this work, we study the inverse problem of identifying complex flocking dynamics in a domain cluttered with obstacles. We get inspiration from animal flocks moving in complex ways with capabilities far beyond what ...
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Reinforcement learning (RL)-based methods have achieved significant success in managing grid-interactive efficient buildings (GEBs). However, RL does not carry intrinsic guarantees of constraint satisfaction, which ma...
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This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented policy loss combining a maximum entropy reinforcement learning objective with a behavioral cloning loss that leverages a forward dynamics model. Then, we propose an algorithm that automatically adjusts the weights of each component in the augmented loss function. Experiments on a variety of continuous control tasks demonstrate that the proposed algorithm outperforms various benchmarks by effectively utilizing available expert observations.
In Part-I, we presented an optimal day-ahead scheduling scheme for dispatching active distribution networks accounting for the flexibility provided by electric vehicle charging stations (EVCSs) and other controllable ...
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Multilayer TiO2:GeO2 alloy and SiO2 coating stacks with >99.999% reflectivity and reduced thermal noise with excellent optical properties are demonstrated. The effect of stepped annealing on the structural properti...
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The paper presents a cradle-to-gate sustainability assessment methodology specifically designed to evaluate aircraft components in a robust and systematic manner. This methodology integrates multi-criteria decision-ma...
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Detecting maturity of fruits and vegetables, especially avocados, is a critical task in modern agriculture and supply chain management. Moreover, the accurate assessment of maturity can improve the harvesting time and...
Detecting maturity of fruits and vegetables, especially avocados, is a critical task in modern agriculture and supply chain management. Moreover, the accurate assessment of maturity can improve the harvesting time and ensure consistent quality for consumers through the supply chain process. A key approach to achieving this is the non-destructive estimation of produce quality. Vision-Based Tactile Sensing (VBTS) technologies, which mimic human tactile perception, offer a novel approach to address this challenge. This paper focuses on the use of two notable VBTS technologies, GelSight and Facebook’s DIGIT sensor. Using these technologies, we developed two novel datasets that assess the avocado maturity using the transformer models, marking a novel contribution in this area. We adapted several transformer architectures to the task, conducting experiments on both image classification and regression to estimate avocado firmness. Among the variants tested, the PoolFormer displayed notable results with accuracy of 92% in detecting avocado maturity level when used with tactile data. The datasets and code used in this study will be shared at this URL.
Tetra Corporation is developing a pulsed power drilling system for the geothermal industry. The system requires a high temperature switching technology to replace the existing technology based on Si thyristors with a ...
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The optoacoustic effect is triggered by directing an optical signal in the air (using laser) to the surface of water, leading to the generation of a corresponding acoustic signal inside the water. Careful modulation o...
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We demonstrate a high-coherence parallel integrated light source utilizing a self-injection locked microcomb and injection locking amplification. Our strategy exhibits a record high on-chip gain of 60 dB, nearly 0 dB ...
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