دانلود مقاله انگلیسی رایگان:به سمت تقویت و یادگیری ایمن در شبکه انبارهای صنعتی - 2020
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  • Towards safe reinforcement-learning in industrial grid-warehousing Towards safe reinforcement-learning in industrial grid-warehousing
    Towards safe reinforcement-learning in industrial grid-warehousing

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Towards safe reinforcement-learning in industrial grid-warehousing


    ترجمه فارسی عنوان مقاله:

    به سمت تقویت و یادگیری ایمن در شبکه انبارهای صنعتی


    منبع:

    Sciencedirect - Elsevier - Information Sciences, 537 (2020) 467-484. doi:10.1016/j.ins.2020.06.010


    نویسنده:

    Per-Arne Andersen ⇑, Morten Goodwin, Ole-Christoffer Granmo


    چکیده انگلیسی:

    Reinforcement learning has shown to be profoundly successful at learning optimal policies for simulated environments using distributed training with extensive compute capacity. Model-free reinforcement learning uses the notion of trial and error, where the error is a vital part of learning the agent to behave optimally. In mission-critical, real-world environments, there is little tolerance for failure and can cause damaging effects on humans and equipment. In these environments, current state-of-the-art reinforcement learning approaches are not sufficient to learn optimal control policies safely. On the other hand, model-based reinforcement learning tries to encode environment transition dynamics into a predictive model. The transition dynamics describes the mapping from one state to another, conditioned on an action. If this model is accurate enough, the predictive model is sufficient to train agents for optimal behavior in real environments. This paper presents the Dreaming Variational Autoencoder (DVAE) for safely learning good policies with a significantly lower risk of catastrophes occurring during training. The algorithm combines variational autoencoders, risk-directed exploration, and curiosity to train deep-q networks inside ”dream” states. We introduce a novel environment, ASRS-Lab, for research in the safe learning of autonomous vehicles in grid-based warehousing. The work shows that the proposed algorithm has better sample efficiency with similar performance to novel model-free deep reinforcement learning algorithms while maintaining safety during training.
    Keywords: Model-based reinforcement learning | Neural networks | Variational autoencoder | Markov decision processes | Exploration | Safe reinforcement learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 18
    حجم فایل: 2201 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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