An Adaptive Reinforcement Learning Path Planning Method for Indoor Complex Dynamic Environments

Path planning in indoor complex dynamic environments faces challenges such as frequent obstacle movements, high environmental uncertainty, and difficulty for agents to achieve stable convergence. Existing algorithms have limitations in adaptability, convergence speed, and path quality. To address these issues, this paper proposes an Adaptive Prioritized Experience Double Deep Q-Network Path Optimization method (APE-DPO), which integrates dynamic perturbation optimization, adaptive exploration rate adjustment, and a prioritized experience replay mechanism to enhance the agent's respon-siveness to dynamic environments and improve policy stability. Experiments conducted in three representative dynamic sce-narios-home, restaurant, and library-demonstrate that APE- DPO outperforms representative reinforcement learning models in the path planning domain, including DQN, DDQN, and their variants, in terms of path length, success rate, and convergence speed. In some scenarios, path length is reduced by 6.9% to 12.5%, showing strong robustness and practical application potential.