  Machine learning (ML) has become an attractive tool in information
processing, however few ML algorithms have been successfully applied in the
quantum domain. We show here how classical reinforcement learning (RL) could be
used as a tool for quantum state engineering (QSE). We employ a measurement
based control for QSE where the action sequences are determined by the choice
of the measurement basis and the reward through the fidelity of obtaining the
target state. Our analysis clearly displays a learning feature in QSE, for
example in preparing arbitrary two-qubit entangled states. It delivers
successful action sequences, that generalise previously found human solutions
from exact quantum dynamics. We provide a systematic algorithmic approach for
using RL algorithms for quantum protocols that deal with non-trivial continuous
state (parameter) space, and discuss on scaling of these approaches for
preparation of arbitrarily large entangled (cluster) states.
