  Due to the scarcity in the wireless spectrum and limited energy resources
especially in mobile applications, efficient resource allocation strategies are
critical in wireless networks. Motivated by the recent advances in deep
reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic
channel access and power control in a wireless interference network. We first
propose a multi-agent DRL algorithm with centralized training (DRL-CT) to
tackle the joint resource allocation problem. In this case, the training is
performed at the central unit (CU) and after training, the users make
autonomous decisions on their transmission strategies with only local
information. We demonstrate that with limited information exchange and faster
convergence, DRL-CT algorithm can achieve 90% of the performance achieved by
the combination of weighted minimum mean square error (WMMSE) algorithm for
power control and exhaustive search for dynamic channel access. In the second
part of this paper, we consider distributed multi-agent DRL scenario in which
each user conducts its own training and makes its decisions individually,
acting as a DRL agent. Finally, as a compromise between centralized and fully
distributed scenarios, we consider federated DRL (FDRL) to approach the
performance of DRL-CT with the use of a central unit in training while limiting
the information exchange and preserving privacy of the users in the wireless
system. Via simulation results, we show that proposed learning frameworks lead
to efficient adaptive channel access and power control policies in dynamic
environments.
