A phasor measurement unit (PMU) is the core measurement component of power system monitoring and analysis. The placement of PMUs directly impacts the state estimation confidence, and hence the optimal PMU placement (OPP), that, minimizing the number of PMUs and ensuring system observability, is appealing to power engineers. However, the current observability analysis mostly relies on topological methods, which cannot reflect the influence of the operating environment. Moreover, intricate OPP models are driving higher demand for efficient solvers. Confronting these challenges, we propose a reinforcement learning graph convolutional network-deep deterministic policy gradient algorithm-based OPP strategy, which effectively captures the system graph structure and PMU state, thereby independently identifying the PMUs value. Furthermore, the degree of system observability is considered, and three observability quantitative indicators are proposed in the OPP strategy, which will enhance the confidence of the perceived state under complex operating environment. The effectiveness of the proposed strategy have been validated in multiple test systems.