  We consider an ambient backscatter communication (AmBC) system aided by an
intelligent reflecting surface (IRS). The optimization of the IRS to assist
AmBC is extremely difficult when there is no prior channel knowledge, for which
no design solutions are currently available. We utilize a deep reinforcement
learning-based framework to jointly optimize the IRS and reader beamforming,
with no knowledge of the channels or ambient signal. We show that the proposed
framework can facilitate efficient AmBC communication with a detection
performance comparable to several benchmarks under full channel knowledge.
