  We consider the dynamic spectrum access (DSA) problem where $K$ Internet of
Things (IoT) devices compete for $T$ time slots constituting a frame. Devices
collectively monitor $M$ events where each event could be monitored by multiple
IoT devices. Each device, when at least one of its monitored events is active,
picks an event and a time slot to transmit the corresponding active event
information. In the case where multiple devices select the same time slot, a
collision occurs and all transmitted packets are discarded. In order to capture
the fact that devices observing the same event may transmit redundant
information, we consider the maximization of the average sum event rate of the
system instead of the classical frame throughput. We propose a multi-agent
reinforcement learning approach based on a stochastic version of Multi-Agent
Deep Deterministic Policy Gradient (MADDPG) to access the frame by exploiting
device-level correlation and time correlation of events. Through numerical
simulations, we show that the proposed approach is able to efficiently exploit
the aforementioned correlations and outperforms benchmark solutions such as
standard multiple access protocols and the widely used Independent Deep
Q-Network (IDQN) algorithm.
