Public service systems in many African regions suffer from delayed emergency response and spatial inequity, causing avoidable suffering. This paper introduces OPTIC-ER, a reinforcement learning (RL) framework for real-time, adaptive, and equitable emergency response. OPTIC-ER uses an attention-guided actor-critic architecture to manage the complexity of dispatch environments. Its key innovations are a Context-Rich State Vector, encoding action sub-optimality, and a Precision Reward Function, which penalizes inefficiency. Training occurs in a high-fidelity simulation using real data from Rivers State, Nigeria, accelerated by a precomputed Travel Time Atlas. The system is built on the TALS framework (Thin computing, Adaptability, Low-cost, Scalability) for deployment in low-resource settings. In evaluations on 500 unseen incidents, OPTIC-ER achieved a 100.00% optimal action selection rate, confirming its robustness and generalization. Beyond dispatch, the system generates Infrastructure Deficiency Maps and Equity Monitoring Dashboards to guide proactive governance and data-informed development. This work presents a validated blueprint for AI-augmented public services, showing how context-aware RL can bridge the gap between algorithmic decision-making and measurable human impact.

翻译：许多非洲地区的公共服务系统存在应急响应延迟和空间不公平问题，导致可避免的苦难。本文提出OPTIC-ER，一种用于实时、自适应且公平应急响应的强化学习（RL）框架。OPTIC-ER采用注意力引导的演员-评论家架构来应对调度环境的复杂性。其核心创新包括编码动作次优性的上下文丰富状态向量，以及惩罚低效的精确奖励函数。训练在基于尼日利亚河流州真实数据的高保真仿真中进行，通过预计算的旅行时间图谱加速。该系统基于TALS框架（薄计算、适应性、低成本、可扩展性）构建，适用于资源匮乏环境部署。在500个未见事故的评估中，OPTIC-ER实现了100.00%的最优动作选择率，证实了其鲁棒性和泛化能力。除调度外，该系统还生成基础设施缺陷地图和公平性监测仪表板，以指导前瞻性治理和数据驱动的发展。本工作为人工智能增强的公共服务提供了一个经过验证的蓝图，展示了情境感知强化学习如何弥合算法决策与可衡量人类影响之间的鸿沟。