  In this work, the novel Image Transformation Sequence Retrieval (ITSR) task
is presented, in which a model must retrieve the sequence of transformations
between two given images that act as source and target, respectively. Given
certain characteristics of the challenge such as the multiplicity of a correct
sequence or the correlation between consecutive steps of the process, we
propose a solution to ITSR using a general model-based Reinforcement Learning
such as Monte Carlo Tree Search (MCTS), which is combined with a deep neural
network. Our experiments provide a benchmark in both synthetic and real
domains, where the proposed approach is compared with supervised training. The
results report that a model trained with MCTS is able to outperform its
supervised counterpart in both the simplest and the most complex cases. Our
work draws interesting conclusions about the nature of ITSR and its associated
challenges.
