In the field of Sequential Decision Making (SDM), two paradigms have historically vied for supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of reconciliation, this paper reviews AP, RL and hybrid methods (e.g., novel learn to plan techniques) for solving Sequential Decision Processes (SDPs), focusing on their knowledge representation: symbolic, subsymbolic or a combination. Additionally, it also covers methods for learning the SDP structure. Finally, we compare the advantages and drawbacks of the existing methods and conclude that neurosymbolic AI poses a promising approach for SDM, since it combines AP and RL with a hybrid knowledge representation.

在顺序决策（SDM）领域，历史上有两种范式争夺霸主地位：自动规划（AP）和强化学习（RL）。本着调和的精神，本文回顾了用于解决顺序决策过程（SDP）的 AP、RL 和混合方法（例如新颖的学习计划技术），重点关注它们的知识表示：符号、子符号或组合。此外，它还涵盖了学习SDP结构的方法。最后，我们比较了现有方法的优点和缺点，并得出结论，神经符号人工智能为 SDM 提供了一种有前途的方法，因为它将 AP 和 RL 与混合知识表示相结合。