학술논문

A Flexible Distributed Building Simulator for Federated Reinforcement Learning
Document Type
Conference
Source
2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Omni-layer Intelligent Systems (COINS), 2022 IEEE International Conference on. :1-6 Aug, 2022
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Training
Buildings
Linearity
Reinforcement learning
Writing
Collaborative work
Data models
Federated Learning
Reinforcement Learning
Building Control
Simulator
Scalability
Language
Abstract
Recently, researches on building control using reinforcement learning are underway to realize a sustainable society. Such building control methods typically use data acquired from sensors and other sources in the building, but such data may contain sensitive information such as human flow, and collecting these in the Cloud for model training can be problematic in terms of privacy and security. With federated learning, it is possible to share and train models for large numbers of buildings while protecting such data. However, it is almost impossible to conduct a model training experiment with federated learning while actually running real buildings. There are several studies of building simulators, but none of them are designed for simultaneous operation of large numbers of buildings, as is the case with federated learning. Therefore, we propose a distributed building simulator for federated reinforcement learning. This simulator is both scalable and flexible, allowing users to conduct experiments scaled to multiple machines without writing complex code for distributed processing. In this paper, we describe the design and implementation of this simulator and demonstrate that it can perform experiments with a large number of buildings, at least 1024 buildings, in a scalable manner.