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Closed loop quantum control uses measurement to control the dynamics of aquantum system to achieve either a desired target state or target dynamics. Inthe case when the quantum Hamiltonian is quadratic in ${x}$ and ${p}$, thereare known optimal control techniques to drive the dynamics towards particularstates e.g. the ground state. However, for nonlinear Hamiltonians such controltechniques often fail. We apply Deep Reinforcement Learning (DRL), where anartificial neural agent explores and learns to control the quantum evolution ofa highly non-linear system (double well), driving the system towards the groundstate with high fidelity. We consider a DRL strategy which is particularlymotivated by experiment where the quantum system is continuously but weaklymeasured. This measurement is then fed back to the neural agent and used fortraining. We show that the DRL can effectively learn counter-intuitivestrategies to cool the system to a nearly-pure `cat' state which has a highoverlap fidelity with the true ground state.

Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential