OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities
Read the original on arXiv AI →OPTIC-ER is a reinforcement learning framework designed to improve emergency response in underserved African communities by providing real‑time, adaptive, and equitable dispatch decisions. It employs an attention‑guided actor‑critic architecture with a Context‑Rich State Vector and a Precision Reward Function, trained on high‑fidelity simulations using real data from Rivers State, Nigeria. In tests on 500 unseen incidents, OPTIC-ER reduced response times by over 35% and surpassed the nearest‑facility heuristic’s 62.94% optimal‑selection rate, while also producing infrastructure deficiency maps and equity monitoring dashboards.
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