Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning

14 Feb 2020Navid NaderializadehJaroslaw SydirMeryem SimsekHosein Nikopour

We propose a mechanism for distributed radio resource management using multi-agent deep reinforcement learning (RL) for interference mitigation in wireless networks. We equip each transmitter in the network with a deep RL agent, which receives partial delayed observations from its associated users, while also exchanging observations with its neighboring agents, and decides on which user to serve and what transmit power to use at each scheduling interval... (read more)

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