Proposal of device control method based on consensus building using reinforcement learning
Proposal of device control method based on consensus building using reinforcement learning
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DOI：
10.1109/icoin50884.2021.9333958
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Isato Oishi;Yuya Tarutani;Y. Fukushima;T. Yokohira
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文献类型：
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作者：
Isato Oishi;Yuya Tarutani;Y. Fukushima;T. Yokohira
通过网络从IoT设备收集各种信息。随着用户对此类设备越来越熟悉，服务需要考虑用户的影响。然而，在具有各种偏好的人共存的环境中，很难设置在所有用户之间建立共识的致动器的参数。传统的方法在用户压力的约束下使功耗最小化。然而，该方法具有计算开销随着设备和用户数量的增加而增加的问题。在这项研究中，我们提出了一种基于共识建立与强化学习的设备控制方法。在所提出的方法中，通过应用强化学习来减少状态，以减少计算开销。作为评估的结果，我们澄清，我们的方法获得的设备参数，提高了1.5倍的奖励相比，传统的方法。此外，我们还澄清了与最佳值相比，可以实现98.6%的奖励值。
Various information is collected from IoT devices through the network. As such device becomes more familiar to the user, services are required to consider the influence of user. However, it is difficult to set the parameters of actuators that build consensus among all users in an environment where people with various preferences coexist. The conventional method minimizes the power consumption under the constraints of the user stress. However, this method has a problem that the calculation overhead is increased as the number of devices and users is increased. In this study, we propose a device control method based on consensus building with reinforcement learning. In the proposed method, the state is reduced by applying reinforcement learning for reducing the calculation overhead. As a result of evaluation, we clarified that our method obtains the device parameters that improve the reward by 1.5 times compared with the conventional method. Moreover, we also clarified that a reward value of 98.6% can be achieved compared to the optimum value.