Deep Reinforcement Learning for Power Converter Control: A Comprehensive Review of Applications and Challenges

Deep reinforcement learning (DRL) has emerged as a promising paradigm for the intelligent control of power electronic converters. It offers adaptability, model-free operation, and real-time decision making in complex, nonlinear, and dynamic environments. This review provides a comprehensive analysis of the state-of-the-art in DRL-based control strategies for various power converter applications. It includes voltage regulation in DC-DC converters connected to DC microgrids, speed control of permanent magnet synchronous motors (PMSM), voltage regulation and frequency modulation in dual active bridge (DAB) converters, maximum power point tracking (MPPT) in solar pv systems, and grid-connected inverter control in both grid-following and grid-forming modes. The paper systematically categorizes the recent literature based on converter topology, control objectives, DRL algorithms used, and implementation frameworks, highlighting the strengths and limitations of each approach. Special attention is given to the design of reward functions and action-state representations. Furthermore, the review identifies key challenges including stability assurance, sample inefficiency, hardware deployment constraints, and lack of standardized benchmarking environments. Finally, research gaps and future directions are outlined, emphasizing the need for physics-informed learning, safe exploration strategies, and hybrid model-based approaches to bridge the gap between academic advances and real-world deployment in power electronic systems.