  Simulated annealing (SA) is a stochastic global optimisation technique
applicable to a wide range of discrete and continuous variable problems.
Despite its simplicity, the development of an effective SA optimiser for a
given problem hinges on a handful of carefully handpicked components; namely,
neighbour proposal distribution and temperature annealing schedule. In this
work, we view SA from a reinforcement learning perspective and frame the
proposal distribution as a policy, which can be optimised for higher solution
quality given a fixed computational budget. We demonstrate that this Neural SA
with such a learnt proposal distribution, parametrised by small equivariant
neural networks, outperforms SA baselines on a number of problems: Rosenbrock's
function, the Knapsack problem, the Bin Packing problem, and the Travelling
Salesperson problem. We also show that Neural SA scales well to large problems
- generalising to significantly larger problems than the ones seen during
training - while achieving comparable performance to popular off-the-shelf
solvers and other machine learning methods in terms of solution quality and
wall-clock time.
