  Following the recent success of Machine Learning tools in wireless
communications, the idea of semantic communication by Weaver from 1949 has
gained attention. It breaks with Shannon's classic design paradigm by aiming to
transmit the meaning, i.e., semantics, of a message instead of its exact
version, allowing for information rate savings. In this work, we apply the
Stochastic Policy Gradient (SPG) to design a semantic communication system by
reinforcement learning, separating transmitter and receiver, and not requiring
a known or differentiable channel model -- a crucial step towards deployment in
practice. Further, we derive the use of SPG for both classic and semantic
communication from the maximization of the mutual information between received
and target variables. Numerical results show that our approach achieves
comparable performance to a model-aware approach based on the reparametrization
trick, albeit with a decreased convergence rate.
