  State-of-the-art Mixed Integer Linear Program (MILP) solvers combine
systematic tree search with a plethora of hard-coded heuristics, such as the
branching rule. The idea of learning branching rules from data has received
increasing attention recently, and promising results have been obtained by
learning fast approximations of the strong branching expert. In this work, we
instead propose to learn branching rules from scratch via Reinforcement
Learning (RL). We revisit the work of Etheve et al. (2020) and propose tree
Markov Decision Processes, or tree MDPs, a generalization of temporal MDPs that
provides a more suitable framework for learning to branch. We derive a tree
policy gradient theorem, which exhibits a better credit assignment compared to
its temporal counterpart. We demonstrate through computational experiments that
tree MDPs improve the learning convergence, and offer a promising framework for
tackling the learning-to-branch problem in MILPs.
