mlsanityData to 7 Oct 2026

Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models

arXiv 2606.04562 ↗First posted 3 Jun 2026cs.AI cs.LG cs.SIcited by 0

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Abstract (arXiv)

Purpose The WHO's COVID-19 non-pharmaceutical interventions (e.g., lockdowns, vaccinations) effectively curb transmission but impose heavy economic strains. Existing research often neglects individual behaviors and falsely assumes perfect infection tracking and flawless policy execution, failing to account for real-world uncertainties and errors. Methods We propose an integrative approach incorporating uncertainties in both epidemic measurement (infections/hospitalizations) and policy implementation. We built a simulation model of 1,000 individuals making real-time choices regarding mask-wearing, vaccination, and shopping. Concurrently, policymakers deploy interventions (lockdowns, mandates) based on health and economic observations. This framework is driven by hierarchical reinforcement learning agents, utilizing deep Q-networks alongside uncertainty-aware policy gradient variants (DDPG and TD3). Results The simulations effectively managed the epidemic's progression. Masking and vaccinations proved highly effective, significantly reducing both the outbreak's peak height and duration. By integrating individual behaviors, policy uncertainties, and multifaceted interventions, our dynamic control approach successfully mitigated the epidemic's impact. Conclusions Our model overcomes previous research limitations by embedding uncertainty and human behavior into public health policy frameworks. The simulation demonstrates that accounting for individual choices and imperfect data is crucial for designing effective interventions during complex pandemics, with masks and vaccines serving as pivotal tools.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 64 references that are in the map. First posted on arXiv 3 Jun 2026.

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15 of 64 references.

References · 15

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  12. 2020VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learningcited by 8
3 more
  1. 2021Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decompositioncited by 6
  2. 2021TyXe: Pyro-based Bayesian neural nets for Pytorchcited by 6
  3. 2021A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learningcited by 1

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