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We discuss the application of each method in detail to evaluate the effectiveness of RL, a state-of-the-art technique in machine learning, compared with conventional solution methods such as LSM in the context of ROV problems. RL, as a state-of-the-art method for sequential decision making (SDM), has demonstrated strong success in solving complex problems in finance, including trading, option pricing, and portfolio management, where decisions are continuous and adaptive. However, our findings suggest that whereas RL has the potential to handle ROV, it is often too sophisticated and unnecessary for the typical structure of ROV and managerial flexibility analyses, where simpler methods such as LSM are usually sufficient. Particularly given the common characteristics of problems in the ROV context, as exemplified by the case study here, the features that highlight RL\u2019s strengths and were key to its development are not present in the ROV context. 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