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 artic...Show MoreMetadata
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.
Published in: 2022 13th International Conference on Information and Communication Technology Convergence (ICTC)
Date of Conference: 19-21 October 2022
Date Added to IEEE Xplore: 25 November 2022
ISBN Information: