Autonomous navigation in energy-intensive industrial environments such as mining facilities and underground infrastructure is challenging due to constrained spaces, dense obstacles, and increasing environmental complexity. Although deep reinforcement learning (DRL) has shown promise for robotic navigation, widely used algorithms often suffer from unstable learning behavior and limited scalability in obstacle-dense settings. In particular, Proximal Policy Optimization (PPO) exhibits high reward variance and slow convergence as environment size and obstacle density increase. This paper presents an application-driven stabilization study of PPO for autonomous navigation in energy-intensive industrial environments. Rather than proposing a new reinforcement learning algorithm, the study integrates practical stabilization mechanisms, including regulated policy updates, mining-aware reward shaping, generalized advantage estimation, and agent-side observation augmentation. The approach is evaluated in grid-based simulation environments that abstract key characteristics of industrial navigation, such as spatial constraints and obstacle density, across four progressively complex use cases. Experimental results demonstrate smoother convergence behavior, reduced reward variance, higher navigation success rates, and fewer collisions compared to PPO, Advantage Actor–Critic (A2C), and Soft Actor–Critic (SAC). These findings highlight the relevance of stabilized reinforcement learning approaches for reliable autonomous navigation in energy-intensive industrial and infrastructure applications.