RelTrans: An Enhancing Offline Reinforcement Learning Model for the Complex Hand Gesture Decision-Making Task.
IEEE Trans. Consumer Electron.(2024)
摘要
As wearable devices gain popularity, gesture recognition technology is becoming increasingly vital. Merely identifying gesture categories is insufficient for devices operating in complex environments. A significant challenge lies in enabling devices to autonomously and efficiently perform gesture recognition tasks, particularly in complex decision-making. Addressing this, this paper introduces an implicit relationship constraint-based offline reinforcement learning model, termed the Relationship Transformer Guided Generative Policy Network (RelTrans), designed for complex gesture decision-making tasks. The model includes an Implicit Constraint-Constructing Network (ICCN) that uses immediate rewards, unbound by predefined reward values, to extract relationship data for guiding the Generative Policy Network (GPN) in predicting action sequences. Additionally, it integrates a knowledge distillation-based Soft-Bias loss function, which not only allows the GPN to leverage ICCN’s implicit constraints but also controls its self-generalization, ensuring effective information exchange and coordinated network updates. These advancements enable the model to comprehend and adapt to higher-level decision-making and reasoning across varying environmental conditions, enhancing the agent and applicability of gesture recognition technology in a broad spectrum of application areas. Extensive experimentation across 19 subtasks in the D4RL offline benchmark suite demonstrates that RelTrans matches or surpasses the performance of previous state-of-the-art approaches in various autonomous decision-making tasks. Our code is open source and available at: https://github.com/Aoudsung/RelTrans.
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关键词
deep learning,data analysis,offline reinforcement learning,hand gesture recognition,decision-making
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