## 1 Introduction
The most commonly used method of path planning is the heuristic algorithm, but when the solution conditions involved are more complex, the traditional heuristic algorithm is inefficient, so many scholars use improved heuristic algorithms to solve path planning problems. [PERSON] et al. [11] proposed an improved A* algorithm including two-way sector expansion and variable-step search strategy for complex terrain and static radar threats in order to quickly avoid static threats. [PERSON] et al. [12] proposed an improved D* algorithm for the path planning problem of complex environments, introducing terrain slope into cost function calculation. [PERSON] et al. [13] proposed a chaotic shared learning particle swarm optimization algorithm (CSPSO) for the path planning and path control problem of USV with multi-objective constraints. However, in the face of changing environments, classical path planning algorithms can no longer meet the real-time and efficient requirements of path planning, so more and more reinforcement learning algorithms are used to solve agent path planning problems. Reinforcement learning is an algorithm that finds the best solution through trial and error in an unknown environment, and reinforcement learning is widely used in path planning because of its own characteristics. In addition, reinforcement learning is also used to solve problems such as image steganography and other problems, and [PERSON] et al. [14] proposed a new end-to-end network architecture based on GAN for image steganism. As a model-free algorithm in reinforcement learning algorithms, the goal of the Q-learning algorithm is to learn a strategy that can handle random transformation and reward problems without adapting to the environment. Q-learning algorithms are widely used because of their simplicity and easy convergence, but Q-learning algorithms also have some shortcomingsin reinforcement learning. In recent years, some scholars have proposed some improved Q-learning algorithms for the shortcomings of the Q-learning algorithm. [PERSON] et al. [15] proposed a multi-agent Q-learning method NashQ in the framework of general and random games, which guarantees convergence considering some highly restrictive assumptions about the stage game form during the learning process, and its advantage is that the complexity is relatively low. In order to overcome the inefficiency of the basic Q-learning algorithm in multi-agent systems, [PERSON] et al. [16] proposed the modular Q-learning algorithm, which decomposes the big problem to be learned into small problems and applies Q-learning to each sub-problem. Using basic Q-learning algorithms in multi-agent environments, where agents are often unable to find answers or take a lot of time to solve problems, [PERSON] et al. [17] proposed a group reinforcement learning algorithm based on the Sarsa method to quickly obtain the optimal strategy for the maximum reward problem. [PERSON] et al. [18] introduced the concept of partially guided Q-learning and initialized the Q table using the pollination algorithm (FPA). [PERSON] et al. [19] proposed a dynamic parameter adjustment strategy and an action deletion mechanism of trial and error method on the basis of Q-learning combined with \(\varepsilon-greedy\), so as to better balance the relationship between exploration and utilization and improve the exploration efficiency of the agent. The above research has made useful progress in improving the efficiency of Q-learning algorithms, however, in large and complex environments, Q-learning algorithms are difficult to achieve the desired results. Therefore, in order to promote the intelligent development of smart parks, give full play to the advantages of unmanned vehicles in global path planning and obstacle avoidance.