@inproceedings{6b1b6a2a82f24030a767182c5aa2e79c,
title = "Design of Distributed Computational Offloading using Ray Framework",
abstract = "Multi-access edge computing has been widely considered as an important technology to support low latency services by reducing application execution latency. In this article we design a distributed computation offloading problem which is solved by using multi-agent deep reinforcement learning. In this scenario, every UE acts as an agent and it tries to maximise its utility in every round of game. The utility of a UE is given as weighted combination of number of processed bits and energy consumed in achieving it. We designed a custom multi-agent scenario for this simulation on Ray platform and trained it using deep reinforcement learning algorithms. The simulation performed showed that the agents reach a stable mean reward.",
keywords = "Distributed offloading, Multi-agent Deep Reinforcement Learning, Ray RLlib",
author = "Sumit Singh and Seo, \{Bong Seok\} and Kim, \{Dong Ho\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Information and Communication Technology Convergence, ICTC 2022 ; Conference date: 19-10-2022 Through 21-10-2022",
year = "2022",
doi = "10.1109/ICTC55196.2022.9952886",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "529--532",
booktitle = "ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence",
}