  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 article 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.
