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

We propose a mechanism for distributed radio resource management using multi-agent deep reinforcement learning to mitigate the interference among concurrent transmissions in wireless networks. We equip each transmitter in the network with a deep RL agent, which receives partial delayed observations from its own associated users, while also exchanging observations with its neighboring agents, and decides on which user to serve and what transmit power level to use at each scheduling interval. We propose a scalable agent design, where the dimensions of its observation and action spaces do not vary with changes in the environment configuration, e.g., in terms of number of transmitter and user nodes. Simulation results demonstrate the superiority of our proposed approach compared to decentralized baselines in terms of the tradeoff between average and 5
^th
percentile user rates, while achieving performance close to, and even in certain cases outperforming, that of a centralized baseline.