  This paper investigates the use of deep reinforcement learning (DRL) in a MAC
protocol for heterogeneous wireless networking referred to as
Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is
partially inspired by the vision of DARPA SC2, a 3-year competition whereby
competitors are to come up with a clean-slate design that "best share spectrum
with any network(s), in any environment, without prior knowledge, leveraging on
machine-learning technique". Specifically, this paper considers the problem of
sharing time slots among a multiple of time-slotted networks that adopt
different MAC protocols. One of the MAC protocols is DLMA. The other two are
TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC
protocols are TDMA and ALOHA. Yet, by a series of observations of the
environment, its own actions, and the resulting rewards, a DLMA node can learn
an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes
according to a specified objective (e.g., the objective could be the sum
throughput of all networks, or a general alpha-fairness objective).
