  Mixed integer linear programs are commonly solved by Branch and Bound
algorithms. A key factor of the efficiency of the most successful commercial
solvers is their fine-tuned heuristics. In this paper, we leverage patterns in
real-world instances to learn from scratch a new branching strategy optimised
for a given problem and compare it with a commercial solver. We propose FMSTS,
a novel Reinforcement Learning approach specifically designed for this task.
The strength of our method lies in the consistency between a local value
function and a global metric of interest. In addition, we provide insights for
adapting known RL techniques to the Branch and Bound setting, and present a new
neural network architecture inspired from the literature. To our knowledge, it
is the first time Reinforcement Learning has been used to fully optimise the
branching strategy. Computational experiments show that our method is
appropriate and able to generalise well to new instances.
