  The ability to handle single molecules as effectively as macroscopic
building-blocks would enable the construction of complex supramolecular
structures inaccessible to self-assembly. The fundamental challenges
obstructing this goal are the uncontrolled variability and poor observability
of atomic-scale conformations. Here, we present a strategy to work around both
obstacles, and demonstrate autonomous robotic nanofabrication by manipulating
single molecules. Our approach employs reinforcement learning (RL), which finds
solution strategies even in the face of large uncertainty and sparse feedback.
We demonstrate the potential of our RL approach by removing molecules
autonomously with a scanning probe microscope from a supramolecular structure
-- an exemplary task of subtractive manufacturing at the nanoscale. Our RL
agent reaches an excellent performance, enabling us to automate a task which
previously had to be performed by a human. We anticipate that our work opens
the way towards autonomous agents for the robotic construction of functional
supramolecular structures with speed, precision and perseverance beyond our
current capabilities.
