Deep Reinforcement Learning in a Dynamic Environment: A Case Study in the Telecommunication Industry
Paper i proceeding, 2022

Reinforcement learning, particularly deep reinforcement learning, has made remarkable progress in recent years and is now used not only in simulators and games but is also making its way into embedded systems as another software-intensive domain. However, when implemented in a real-world context, reinforcement learning is typically shown to be fragile and incapable of adapting to dynamic environments. In this paper, we provide a novel dynamic reinforcement learning algorithm for adapting to complex industrial situations. We apply and validate our approach using a telecommunications use case. The proposed algorithm can dynamically adjust the position and antenna tilt of a drone-based base station to maintain reliable wireless connectivity for mission-critical users. When compared to traditional reinforcement learning approaches, the dynamic reinforcement learning algorithm improves the overall service performance of a drone-based base station by roughly 20%. Our results demonstrate that the algorithm can quickly evolve and continuously adapt to the complex dynamic industrial environment.

Software Engineering

Emergency Communication Network

Reinforcement Learning

Machine Learning

Författare

Hongyi Zhang

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Jingya Li

Ericsson AB

Zhiqiang Qi

Ericsson AB

Xingqin Lin

Ericsson AB

Anders Aronsson

Ericsson AB

Jan Bosch

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Helena Holmström Olsson

Malmö universitet

Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022

68-75
9781665461528 (ISBN)

48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022
Gran Canaria, Spain,

Ämneskategorier

Robotteknik och automation

Datavetenskap (datalogi)

Datorsystem

DOI

10.1109/SEAA56994.2022.00019

Mer information

Senast uppdaterat

2023-10-26