  Closed loop quantum control uses measurement to control the dynamics of a
quantum system to achieve either a desired target state or target dynamics. In
the case when the quantum Hamiltonian is quadratic in ${x}$ and ${p}$, there
are known optimal control techniques to drive the dynamics towards particular
states e.g. the ground state. However, for nonlinear Hamiltonians such control
techniques often fail. We apply Deep Reinforcement Learning (DRL), where an
artificial neural agent explores and learns to control the quantum evolution of
a highly non-linear system (double well), driving the system towards the ground
state with high fidelity. We consider a DRL strategy which is particularly
motivated by experiment where the quantum system is continuously but weakly
measured. This measurement is then fed back to the neural agent and used for
training. We show that the DRL can effectively learn counter-intuitive
strategies to cool the system to a nearly-pure `cat' state which has a high
overlap fidelity with the true ground state.
