  Particle physics experiments often require the reconstruction of decay
patterns through a hierarchical clustering of the observed final-state
particles. We show that this task can be phrased as a Markov Decision Process
and adapt reinforcement learning algorithms to solve it. In particular, we show
that Monte-Carlo Tree Search guided by a neural policy can construct
high-quality hierarchical clusterings and outperform established greedy and
beam search baselines.
