| Title |
Consensus Building using Deep Reinforcement Learning for Energy Management |
| Authors |
(Yuya Tarutani) ; (Isato Oishi) ; (Yukinobu Fukushima) ; (Tokumi Yokohira) |
| DOI |
https://doi.org/10.5573/IEIESPC.2022.11.4.284 |
| Keywords |
Internet of things; Reinforcement learning; Consensus builder |
| Abstract |
A variety of information is collected from IoT devices. As those devices become more familiar to users, network services must consider the influence of the user. We propose a method to maximize the value from power consumption and minimize the cost incurred to ensure user satisfaction. However, one problem is that user satisfaction cannot increase because it is considered a constraint on power consumption. In this paper, we propose a consensus building method to minimize power consumption and maximize user satisfaction. An exhaustive search incurs a large calculation overhead to determine device parameters. Thus, the proposed method uses reinforcement learning to solve this problem. From its evaluation, we clarify that the proposed method attains about 1.5 times the total reward compared with the conventional method. Moreover, we also clarify that 99.9% of the total reward can be achieved, compared to the exhaustive search. |