Application and Optimization of Reinforcement Learning Based on Deep Q-Network (DQN) in Complex Environments

Reinforcement Learning, as an important branch of machine learning, is dedicated to learning how to make optimal decisions to maximize the cumulative reward in an uncertain environment. Deep Q-Network (DQN) combines the advantages of deep learning and reinforcement learning and approximates the Q-value function through deep neural network. Despite the remarkable success of DQN in complex tasks, there are still challenges in continuous action space processing, high-dimensional state space exploration efficiency, adaptability to environmental changes, and delayed reward issues. To overcome these limitations, this paper proposes an optimization of the DQN algorithm, Double-Deep Q-Network, and the concept of DDQN. This paper first analyzes the challenges faced by DQN in complex environments and outline the introduction of DDQN and its comparative analysis with DQN. Then, a series of optimization measures are introduced, including network structure optimization, target network update strategy improvement, dynamic adjustment of exploration strategy and ensemble learning, to improve the performance and stability of DDQN. In addition, the application cases of DDQN in different fields such as autonomous driving, and game AI are explored, and the corresponding improvement strategies are analyzed. Finally, the optimization strategies of DDQN in practical applications are summarized, and its potential applications in future complex decision-making problems are prospected. Through continuous technological innovation and algorithm optimization, DDQN is expected to play a greater role in a wider range of fields and promote the development of intelligent decision systems.