A Systematic Review of Reinforcement Learning for Dynamic Risk Assessment

Traditional risk assessment méthodologies are often inadequate in dynamic environments due to their reliance on static historical data. A viable substitute for adaptive, sequential decision-making in the face of uncertainty is Reinforcement Learning (RL). However, the research landscape is fragmented, lacking a unified framework to guide the selection of RL paradigms, such as risk-sensitive, safe, and robust RL, for specific risk categories. To close this gap, this review examines RL’s use in risk assessment in a methodical manner. We define a conceptual framework for classifying risk-aware reinforcement learning techniques, compare and contrast their advantages and disadvantages, and pinpoint the main obstacles to dependable implementation. The substantial promise of RL is confirmed by our study, which is based on a systematic evaluation of recent literature (2018–2024) in the fields of finance, autonomous systems, and healthcare. Major issues still exist, nevertheless, such as sample inefficiency, performance-safety trade-offs, and a discrepancy between theoretical assurances and actual dependability. We come to the conclusion that creating hybrid models, setting strict standards, and giving safety and robustness top priority will be necessary for future development in order to facilitate deployment in high-stakes, real-world situations.