  Traditional radio systems are strictly co-designed on the lower levels of the
OSI stack for compatibility and efficiency. Although this has enabled the
success of radio communications, it has also introduced lengthy standardization
processes and imposed static allocation of the radio spectrum. Various
initiatives have been undertaken by the research community to tackle the
problem of artificial spectrum scarcity by both making frequency allocation
more dynamic and building flexible radios to replace the static ones. There is
reason to believe that just as computer vision and control have been overhauled
by the introduction of machine learning, wireless communication can also be
improved by utilizing similar techniques to increase the flexibility of
wireless networks. In this work, we pose the problem of discovering low-level
wireless communication schemes ex-nihilo between two agents in a fully
decentralized fashion as a reinforcement learning problem. Our proposed
approach uses policy gradients to learn an optimal bi-directional communication
scheme and shows surprisingly sophisticated and intelligent learning behavior.
We present the results of extensive experiments and an analysis of the fidelity
of our approach.
