## 1 Introduction  
Reinforcement Learning (RL) has emerged as a powerful framework for solving sequential decision-making problems, where an agent interacts with an environment to learn optimal behaviors by maximizing cumulative rewards [1]. This ability to adapt through interaction makes RL particularly suitable for mobile robot path planning in dynamic and uncertain environments. Traditional path planning algorithms, such as A* and [PERSON]'s, excel in static and well-structured settings but falter in applications characterized by uncertainty, unpredictability, and incomplete knowledge of the environment. For instance, search-and-rescue operations in disaster-stricken areas often involve navigating through unknown terrains filled with debris and dynamic obstacles. Similarly, autonomous delivery robots operating in crowded urban environments must make real-time decisions to avoid pedestrians and other obstacles while efficiently reaching their destinations. In such scenarios, RL offers a robust solution by learning policies that directly map sensory inputs to actions, enabling real-time decision-making and adaptability [2]. Deep Reinforcement Learning (DRL), a subset of RL that leverages deep neural networks, has significantly advanced the capabilities of RL by addressing challenges associated with high-dimensional state-action spaces and continuous control. Unlike traditional RL, which struggles to scale in complex environments, DRL employs neural networks to approximate value functions and policies, enabling efficient learning and decision-making in scenarios involving raw sensory inputs like camera images or LiDAR data [3]. This scalability and versatility make DRL an ideal framework for tackling complex navigation problems, such as autonomous navigation in cluttered indoor spaces, multi-agent coordination, and socially aware robot path planning. Techniques, such as Proximal Policy Optimization (PPO) [4], Deep Deterministic Policy Gradient (DDPG) [3], and Soft Actor-Critic (SAC) [2] have demonstrated remarkable performance in such applications, achieving state-of-the-art results across a variety of benchmark environments. This paper presents a comprehensive review of DRL techniques for mobile robot path planning, categorizing existing methods and analyzing their strengths and limitations. It explores how DRL can address challenges in path planning, including navigation in dynamic and uncertain environments, balancing efficiency with safety, and generalizing to unseen scenarios. The review focuses on the application of DRL in tasks requiring continuous control, adaptive decision-making, and integration with multimodal sensory inputs. Several reviews of DRL solutions for mobile robot path planning have been presented in the literature, offering broad overviews of existing methods and their applications. However, this work differentiates itself by providing a structured and standardized analysis of DRL techniques through a unified notation framework. Specifically, we categorize various DRL methods and reformulate their mathematical formulations under a consistent notation, facilitating direct comparisons across different approaches. Furthermore, for each category, multiple representative solutions are analyzed, with their key components restructured to maintain uniformity in presentation. A notable contribution of this review is the inclusion of original block diagrams and pseudocode representations for nearly all solutions discussed, offering a clearer understanding of their working principles and implementation details. These enhancements provide a more accessible and structured perspective on DRL-based path planning, allowing researchers and practitioners to better interpret, compare, and apply these methodologies in real-world scenarios.