Traffic Optimization in Satellites Communications: A Multi-agent Reinforcement Learning Approach | IEEE Conference Publication | IEEE Xplore

Traffic Optimization in Satellites Communications: A Multi-agent Reinforcement Learning Approach


Abstract:

Past few years have witnessed the compelling applications of the satellite communications and networking in our daily life. Due to the extremely high moving speeds and li...Show More

Abstract:

Past few years have witnessed the compelling applications of the satellite communications and networking in our daily life. Due to the extremely high moving speeds and limited networking resources of LEO satellites, how to optimize inter-satellite traffic has received amount of attention from both academia and industry. In this paper, we proposed a hybrid satellites network traffic control paradigm. In our architecture, the centralized platform collect the global state and the joint action from each agent during the training phase to ease the training, and during execution, the each agent can return the action to the local state through the trained policy. Besides, we adopt a multiagent actor-critic algorithms named Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments(MADDPG) to our architecture. In addition, some simulation results are presented to evaluate the correctness of our architecture and algorithm.
Date of Conference: 15-19 June 2020
Date Added to IEEE Xplore: 27 July 2020
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Conference Location: Limassol, Cyprus

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