دانلود مقاله انگلیسی رایگان:طبقه بندی اولیه انطباقی توالی های زمانی با استفاده از یادگیری تقویتی عمیق - 2020
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  • Adaptive early classification of temporal sequences using deep reinforcement learning Adaptive early classification of temporal sequences using deep reinforcement learning
    Adaptive early classification of temporal sequences using deep reinforcement learning

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Adaptive early classification of temporal sequences using deep reinforcement learning


    ترجمه فارسی عنوان مقاله:

    طبقه بندی اولیه انطباقی توالی های زمانی با استفاده از یادگیری تقویتی عمیق


    منبع:

    Sciencedirect - Elsevier - Knowledge-Based Systems, 190 (2020) 105290. doi:10.1016/j.knosys.2019.105290


    نویسنده:

    Coralie Martinez a,∗, Emmanuel Ramasso b, Guillaume Perrin a, Michèle Rombaut c


    چکیده انگلیسی:

    In this article, we address the problem of early classification (EC) of temporal sequences with adaptive prediction times. We frame EC as a sequential decision making problem and we define a partially observable Markov decision process (POMDP) fitting the competitive objectives of classification earliness and accuracy. We solve the POMDP by training an agent for EC with deep reinforcement learning (DRL). The agent learns to make adaptive decisions between classifying incomplete sequences now or delaying its prediction to gather more measurements. We adapt an existing DRL algorithm for batch and online learning of the agent’s action value function with a deep neural network. We propose strategies of prioritized sampling, prioritized storing and random episode initialization to address the fact that the agent’s memory is unbalanced due to (1): all but one of its actions terminate the process and thus (2): actions of classification are less frequent than the action of delay. In experiments, we show improvements in accuracy induced by our specific adaptation of the algorithm used for online learning of the agent’s action value function. Moreover, we compare two definitions of the POMDP based on delay reward shaping against reward discounting. Finally, we demonstrate that a static naive deep neural network, i.e. trained to classify at static times, is less efficient in terms of accuracy against speed than the equivalent network trained with adaptive decision making capabilities
    Keywords: Early classification | Adaptive prediction time | Deep reinforcement learning | Temporal sequences | Double DQN | Trade-off between accuracy vs. speed


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 10
    حجم فایل: 799 کیلوبایت

    قیمت: رایگان


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