## 10 Conclusions and Future Directions  
This review provides a comprehensive analysis of the application of Deep Reinforcement Learning (DRL) in mobile robot path planning, showcasing its potential to tackle dynamic and uncertain environments where traditional methods often fall short. By leveraging neural networks to handle high-dimensional state-action spaces, DRL enables mobile robots to make real-time decisions with enhanced efficiency and adaptability. The categorized exploration of value-based methods, policy-based approaches, and hybrid frameworks demonstrates the breadth and versatility of DRL techniques in addressing complex navigation challenges. Key advancements, such as Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and hybrid frameworks, have delivered a state-of-the-art performance in scenarios ranging from indoor navigation to multi-agent coordination. Despite this progress, limitations persist in scalability, safety, interpretability, and generalization to unseen environments. Addressing these challenges is essential for fully harnessing the transformative potential of DRL in autonomous navigation. The future of DRL in mobile robot path planning lies in overcoming these limitations and expanding its applicability to real-world scenarios. One critical area is scalability, as current DRL models often struggle with large and complex environments. Distributed and hierarchical learning frameworks could address this by decomposing navigation tasks into subproblems. For instance, a warehouse robot might use a hierarchical approach to first navigate to a specific aisle and then locate a target object within it. Real-time performance improvements will also be essential, especially for logistics robots in high-demand environments like fulfillment centers. Ensuring safety in navigation is another crucial direction, particularly for robots operating in human-centric environments. By integrating DRL with safety constraints and verification mechanisms, robots can learn to avoid risky behaviors. Autonomous delivery robots in crowded urban areas, for example, could benefit from policies that account for pedestrian movements and dynamic obstacles while maintaining a reliable path to their destination. The gap between simulation and reality remains a significant challenge. While simulation environments are invaluable for training DRL models, real-world deployment often suffers from discrepancies in environmental conditions. Techniques like domain adaptation and transfer learning could help bridge this gap. For example, autonomous vehicles trained on simulated highways could adapt their policies to real-world conditions involving variable weather, road textures, and traffic dynamics. Coordination among multiple robots presents another promising avenue for DRL. Multi-agent reinforcement learning frameworks could promote collaboration and efficient resource use in shared environments. Search-and-rescue missions, for instance, could benefit from teams of drones and ground robots working together to navigate and locate survivors in disaster-stricken areas. Similarly, such frameworks could optimize fleets of warehouse robots for collaborative tasks, reducing bottlenecks and improving throughput. Socially aware navigation is another frontier where DRL could play a transformative role. Incorporating human preferences and social norms into reward functions can help robots operate seamlessly in public spaces. Service robots in airports, shopping malls, or hospitals must navigate crowded areas while respecting personal space and pedestrian flow patterns. These considerations could enhance their acceptance and utility in everyday human environments. Energy efficiency is an often-overlooked aspect of path planning but is critical for battery-powered mobile robots. Reward functions that factor in energy consumption and optimize for sustainable operation could extend the operational range of robots in applications like agricultural fieldwork or remote inspection tasks.