Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episo...
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Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episodic resets when a failure *** manual resets are generally unavailable in autonomous robots,we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failure-induced *** multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and,more importantly,deciding which previous state is the best to return to for efficient *** failure prevention reduces potential failures by predicting and excluding possible unsafe actions in specific *** simulations and real-world experiments are used to validate our algorithm with the results showing a significant reduction in the number of resets and failures during the learning.
In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive stepsizes. To address this challenge, we p...
Switching power supplies are widely used in aerospace, new energy and many other fields. During long-term operation, various electronic components in these supplies experience performance degradation due to continuous...
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Using machine vision for meter reading significantly enhances the efficiency of industrial monitoring. However, meters in outdoor environment are often subject to the noise such as rain and fog, which affect the accur...
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This paper proposes a data-driven control (DDC) strategy for nonlinear automated vehicles, employing a multidescription coding (MDC) mechanism based on scalar quantization to address the challenges of data dropouts an...
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Estimating the Worst-Case Execution Time (WCET) of programs in an embedded multi-core environment is fundamental for schedulability analysis. In this paper, we propose a framework for calculating the WCET of programs ...
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Sensitivity analysis is a powerful tool that can be utilized for reducing the size of the design space at hand to explore and significantly reduce the computational burden of the optimization process. This is achieved...
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Automatic defect detection on wood surface is essential for ensuring product quality. Semantic segmentation methods have shown outstanding performance in wood defect detection. However, it is costly to acquire correct...
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Recent advancements in autonomous vehicle research highlight the importance of Machine Learning (ML) models in tasks like motion planning, trajectory prediction, and emergency management. To support AI development, we...
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Type 1 diabetes is one of the major concerns in current medical studies, as the World Health Organisation plans to reduce mortality due to such disease by one third by 2030. Standard clinical practice involves self-ad...
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