This chapter presents a theoretical framework for railway 3D spatial intelligent location design based on deep reinforcement learning. The chapter begins with a comprehensive review of reinforcement learning (RL) methods and their key components in addressing optimization problems. It then critically evaluates the applicability and limitations of traditional reinforcement learning algorithms, such as Q-Learning, Deep Q-Network (DQN), and Deep Policy Gradient (DPG), in railway 3D spatial alignment optimization. Thereby, a deep reinforcement learning framework is proposed. Furthermore, the chapter provides detailed methodologies for redefine the reinforcement learning elements for railway location design, including the environment, agent, state, action, and reward, and provide corresponding mathematical descriptions of the state space, action space, and reward function specifically for railway location design, which are crucial for railway alignment design under high-dimensional, coupled, and dynamic conditions. Deep Deterministic Policy Gradient (DDPG) algorithm integrates policy gradient and deep Q-network techniques to effectively handle optimization problems involving continuous state and action spaces. The proposed framework demonstrates superior convergence and global search capabilities. Based on the computational process of the DDPG algorithm, corresponding programs are developed, providing a solid theoretical foundation and practical implementation for achieving global optimization in railway three-dimensional spatial intelligent location design. At the end of the chapter, it introduces the application of deep reinforcement learning methods to solve three-dimensional spatial railway alignment optimization design problems.