Progress and Challenges in Applying Deep Reinforcement Learning to Intelligent Navigation

This article mainly discusses the application methods, progress and challenges of deep reinforcement learning (DRL) in intelligent navigation. With the development of computer and artificial intelligence technology, deep learning and reinforcement learning are combined to form deep reinforcement learning. This method shows significant advantages in processing high-dimensional state spaces and complex decision-making tasks. The article first reviews traditional navigation methods, including simulated annealing methods, artificial potential field methods, and fuzzy logic methods. Then it analyzes graph-based methods such as A* algorithm, probabilistic landmark method, and rapid exploration of random trees, as well as bionic intelligence methods such as Genetic algorithm, artificial neural network, ant colony optimization and particle swarm optimization, etc. Subsequently, the article introduces in detail the basic principles of reinforcement learning and its value-oriented, strategy-oriented and combination-oriented methods, focusing on the specific application of deep reinforcement learning in navigation tasks, such as deep Q network (DQN), deep deterministic strategy gradient (DDPG) and dominant actor-critic (A2C) algorithms. Finally, the article discusses the advantages, challenges and future development directions of deep reinforcement learning in navigation applications, emphasizing the technology's path planning and decision-making optimization capabilities in complex dynamic environments.