  This paper proposes a multi-agent reinforcement learning based medium access
framework for wireless networks. The access problem is formulated as a Markov
Decision Process (MDP), and solved using reinforcement learning with every
network node acting as a distributed learning agent. The solution components
are developed step by step, starting from a single-node access scenario in
which a node agent incrementally learns to control MAC layer packet loads for
reining in self-collisions. The strategy is then scaled up for multi-node
fully-connected scenarios by using more elaborate reward structures. It also
demonstrates preliminary feasibility for more general partially connected
topologies. It is shown that by learning to adjust MAC layer transmission
probabilities, the protocol is not only able to attain theoretical maximum
throughput at an optimal load, but unlike classical approaches, it can also
retain that maximum throughput at higher loading conditions. Additionally, the
mechanism is agnostic to heterogeneous loading while preserving that feature.
It is also shown that access priorities of the protocol across nodes can be
parametrically adjusted. Finally, it is also shown that the online learning
feature of reinforcement learning is able to make the protocol adapt to
time-varying loading conditions.
