Solving jigsaw puzzles requires to grasp the visual features of a sequence of patches and to explore efficiently a solution space that grows exponentially with the sequence length. Therefore, visual deep reinforcement learning (DRL) should answer this problem more efficiently than optimization solvers coupled with neural networks. Based on this assumption, we introduce Alphazzle, a reassembly algorithm based on single-player Monte Carlo Tree Search (MCTS). A major difference with DRL algorithms lies in the unavailability of game reward for MCTS, and we show how to estimate it from the visual input with neural networks. This constraint is induced by the puzzle-solving task and dramatically adds to the task complexity (and interest!). We perform an in-deep ablation study that shows the importance of MCTS and the neural networks working together. We achieve excellent results and get exciting insights into the combination of DRL and visual feature learning.

翻译：解决拼图问题需要掌握一系列图像块的视觉特征，并高效探索一个随序列长度呈指数级增长的解空间。因此，视觉深度强化学习（DRL）应能比基于优化求解器与神经网络相结合的方法更有效地解决这一问题。基于这一假设，我们提出Alphazzle——一种基于单人蒙特卡洛树搜索（MCTS）的重组算法。与DRL算法的主要区别在于MCTS无法获得游戏奖励，我们展示了如何通过神经网络从视觉输入中估计该奖励。这一约束由拼图求解任务本身引发，显著增加了任务的复杂性（以及趣味性！）。我们进行了深入的消融研究，表明MCTS与神经网络的协同工作至关重要。我们取得了优异的结果，并在DRL与视觉特征学习的结合方面获得了令人兴奋的见解。