As the degree of informatization continues to deepen, the application of robots is becoming more and more extensive. However, in many cases, robots need to work in a constantly changing and complex environment. Because of the inability to obtain environmental information in advance, it is often difficult to plan a suitable path for a robot. To solve this problem, this paper proposes an method for robot path planning. This method uses the grid method to establish an environmental model, uses the number of exploration steps to define the return value, and continuously optimizes the path through reinforcement learning. At the same time, aiming at the problem of the balance between the exploration and utilization of the environment in reinforcement learning, a variable ε-decreasing action selection strategy and learning rate selection method are proposed to make the exploration factor dynamically change as the agent explores the environment, thereby accelerating the convergence speed of the learning algorithm. Simulation results show that this method can realize autonomous navigation and fast path planning of mobile robots in complex environments, compared with traditional algorithms, under the premise of obtaining the same path length, the number of iterations is reduced by approximately 32%, effectively speeding up the convergence speed.