Deep Reinforcement Learning for DC–DC Boost Converter Control: Classical Foundations, Design Taxonomy, and Hardware-Oriented Validation

The DC–DC boost converter is a challenging control target because of its nonlinear dynamics, wide operating range, and non-minimum-phase behavior under continuous conduction mode. These control challenges are particularly pronounced under large-signal transients, parameter variations, constant-power-load effects, and hardware constraints. This review examines deep reinforcement learning-based control of DC–DC boost converters from an engineering-oriented perspective. It covers learning-assisted classical control, direct duty-cycle control, and hybrid architectures, with attention to action design, reward formulation, observation timing, safety constraints, and validation fidelity. A structured search of Scopus, Web of Science Core Collection, and IEEE Xplore was used to identify boost-specific studies and transferable adjacent-converter evidence. Rather than ranking algorithms alone, the review organizes the literature around converter-aware and hardware-oriented learning control. The review argues that recent progress should not be interpreted as a simple replacement of classical control by deep reinforcement learning. Accordingly, algorithm choice, physical knowledge, action and reward design, observation timing, safety constraints, and validation fidelity are treated jointly. The available evidence suggests that progress toward credible practical deployment requires integrating converter physics, bounded or hybrid control authority, explicit safety constraints, and hardware-oriented validation.