  We explore the use of policy gradient methods in reinforcement learning for
quantum control via energy landscape shaping of XX-Heisenberg spin chains in a
model agnostic fashion. Their performance is compared to finding controllers
using gradient-based L-BFGS optimisation with restarts, with full access to an
analytical model. Hamiltonian noise and coarse-graining of fidelity
measurements are considered. Reinforcement learning is able to tackle
challenging, noisy quantum control problems where L-BFGS optimization
algorithms struggle to perform well. Robustness analysis under different levels
of Hamiltonian noise indicates that controllers found by reinforcement learning
appear to be less affected by noise than those found with L-BFGS.
