The standard Q-Learning algorithm based on Markov decision process in reinforcement learning can obtain an optimum path, but the method has the shortcomings of slow convergence rate and low planning efficiency, and thus can not be directly applied to the real environment. This paper proposes a Q-Learning path planning algorithm for mobile robots based on the potential energy field knowledge.By introducing the potential energy value into the environment as the search heuristic information to initialize the Q value, the rapid convergence of the mobile robot can be guided in the early stage of learning, and the blindness of the traditional reinforcement learning process is avoided, which makes it suitable for direct learning in a real environment. The simulation result shows that: Compared with existing algorithms, the proposed algorithm not only improves the convergence speed, but also shortens the learning time, which can make the mobile robot find a better collision-free path quickly.