<p>Geological and grade uncertainty influence the economic performance and feasibility of underground mining projects, particularly in sublevel stoping operations. Traditional deterministic approaches to stope layout optimization fail to account for this uncertainty, often leading to suboptimal designs and increased financial risks. This research addresses these challenges by proposing a novel reinforcement learning (RL) framework to optimize underground stope layouts under uncertainty. The framework employs proximal policy optimization (PPO), a deep reinforcement learning algorithm, to balance profitability and financial risk using the conditional-value-at-risk metric. An ensemble of geostatistical realizations captures the uncertainty in mineral grade and geological variability, enabling the evaluation of risk-adjusted profit distributions for stope designs. The proposed methodology generates a range of design options tailored to varying risk tolerances, enabling decision-makers to align designs with project-specific objectives and risk attitudes. A case study of an underground gold deposit, comprising 840,000 blocks and 100 realizations, demonstrates the effectiveness of the framework. The RL-based designs outperform conventional deterministic methods by forming an efficient frontier of stope layout options that balance expected profit and uncertainty. At the opportunity-seeking behavior, a 5% increased profit was obtained by the generated layout when compared to a deterministic optimization benchmark. Risk-averse layouts exhibit higher confidence in profitability and lower uncertainty, while opportunity-seeking designs maximize expected returns. The results highlight the framework’s ability to transform geological uncertainty into actionable insights, enhancing decision-making in underground mine design.</p>