Path planning is a core issue in fields such as robotics, autonomous driving, and intelligent transportation. Aiming at the path planning problem in complex dynamic environments, the combination of RL (reinforcement learning) and TL (transfer learning) provide new ideas. The limitations of traditional path planning methods were first reviewed, followed by a systematic analysis of the key technologies of RL and TL in path planning as well as their integration potential. It is found that the dynamic decision-making advantage of RL and the cross-domain knowledge reuse capability of TL form a complement. Their integrated architecture can effectively resolve the contradiction between sample efficiency and generalization of path planning in complex dynamic environments. Meanwhile, domain similarity quantification, negative transfer suppression, and safety interpretability were identified as the core research pain points in this field. Finally, future research directions were prospected from aspects such as efficient knowledge transfer and multi-task collaborative optimization.