The Central Pattern Generator (CPG) is adept at generating rhythmic gait patterns characterized by consistent timing and adequate foot clearance. Yet, its open-loop configuration often fails to adjust the system’s control performance in response to environmental variations. On the other hand, Reinforcement Learning (RL), celebrated for its model-free properties, has gained significant traction in robotics due to its inherent adaptability and robustness. However, initiating traditional RL approaches from the ground up presents a risk of converging to suboptimal local minima and slow learning convergence. In this paper, we propose a quadruped locomotion framework-called SYNLOCO-by synthesizing CPG and RL, which can ingeniously integrate the strengths of both methods, enabling the development of a locomotion controller that is both stable and natural with partial state observations (e.g., no velocity measurements). To optimize the learning trajectory of SYNLOCO, a two-phase training strategy is presented. Both ablation analysis and experimental comparison are performed using a real quadruped robot under varied conditions, including distinct velocities, terrains, and payload capacities. The experiments showcase SYNLOCO’s efficiency in producing consistent and clear-footed gaits across diverse scenarios, despite no velocity measurements. The developed controller exhibits resilience against substantial parameter variations, underscoring its potential for robust real-world applications.