A Distributed Reinforcement Learning approach for Power Control in Wireless Networks

This paper tackles the power control problem in the context of wireless networks. The development of intelligent services based on widespread smart devices with limited energy storage capabilities and high interference sensitivity is heavily bounded by the energy consumption required for communication. For addressing this issue, a decentralized control approach based on multi-agent reinforcement learning has been developed. The most interesting feature of the proposed solution consists in its scalability and low complexity. As a consequence, the proposed approach can be deployed in presence of sensor nodes with low processing and communication capabilities. Simulations are presented to validate the proposed solution.