  We introduce a new semantic communication mechanism - SemanticRL, whose key
idea is to preserve the semantic information instead of strictly securing the
bit-level precision. Unlike previous methods that mainly concentrate on the
network or structure design, we revisit the learning process and point out the
semantic blindness of commonly used objective functions. To address this
semantic gap, we introduce a schematic shift that learns from semantic
similarity, instead of relying on conventional paired bit-level supervisions
like cross entropy and bit error rate. However, developing such a semantic
communication system is indeed a nontrivial task considering the
non-differentiability of most semantic metrics as well as the instability from
noisy channels. To further resolve these issues, we put forward a self-critic
reinforcement learning (RL) solution which allows an efficient and stable
learning on any user-defined semantic measurement, and take a step further to
simultaneously tackle the non-differentiable semantic channel optimization
problem via self-critic stochastic iterative updating (SCSIU) training on the
decoupled semantic transceiver. We have firstly tested the proposed method in
the challenging European-parliament dataset, which confirms the superiority of
our method in revealing the semantic meanings, and better handling the semantic
noise. Apart from the experimental results, we further provide an in-depth look
at how the semantic model behaves, along with its superb generalization ability
in real-life examples. An RL-based image transmission extension is also
exemplified, so as to prove the generalization ability and motivate future
discussion.
