Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs

Wireless communication networks constitute complex systems This interest can be attributed to the innate characteristics of demanding careful optimization of network procedures GNNs, which enable a scalable solution and exhibit inductive to attain predefined performance objectives. Multi-agent deep capability and, thanks to the permutation equivariance property, reinforcement learning (MADRL), owing to its inherent advantages, increased generalization. Notably, these properties find has emerged as a promising strategy for the optimization practical application in works such as [5], where GNNs are of a variety of network problems. Nevertheless, the practical harnessed to capture the dynamic structure of fading channel implementation of MADRL in real systems is hindered by states for the purpose of learning optimal resource allocation challenges related to convergence, which continue to constitute policies in wireless networks. Another domain that has witnessed an active area of research. These challenges encompass the substantial utilization of GNNs is channel management non-stationarity of the environment, the partial observability of within wireless local area networks (WLANs), as evidenced the state, as well as the coordination and cooperation among by works such as [6] and [7]. A notable insight derived from agents [1, 2]. To this end, this paper elucidates the role of the study by Gao et al. [6] is the inherent property of GNNs to leveraging graph structures as an effective means to account provide decentralized inference, rendering them a viable and for non-stationarity in MADRL systems by introducing a promising approach for the practical implementation of overthe-air relational inductive bias in the collective decision-making MADRL systems.