Adaptive Q-learning path planning algorithm based on virtual target guidance

When the classical reinforcement learning algorithm is used for robot path planning in unknown environments, there are problems such as low exploration efficiency, slow convergence speed, easy to fall into terrain traps, and lack of intermediate states in the learning process, resulting in blindness in exploration. To solve the above problems, a dual memory mechanism, a virtual target guidance method and an adaptive greedy factor were designed, and an adaptive Q-Learning algorithm based on Virtual Target Guidance (VTGA-Q-Learning) was proposed. To verify the performance of the new algorithm, four kinds of environment maps were designed, and the simulation experiments were compared with other improved algorithms. Furthermore, a virtual simulation experiment of the four-wheel drive McNum wheel robot was carried out to simulate the real environment and verify the performance of the algorithm. Experimental results showed that the proposed new algorithm significantly reduced the number of iterations, improved the convergence speed of reinforcement learning, and had good robustness to complex environments, which could effectively avoid terrain traps, improve the performance of mobile robot navigation system and provided a reference for mobile robot autonomous path planning. © 2024 CIMS. All rights reserved.