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

arXiv: https://arxiv.org/abs/2606.04562 · HTML: https://mlsanity.com/paper/2606.04562/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 64 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Action Robust Reinforcement Learning and Applications in Continuous Control](https://mlsanity.com/paper/1901.09184/) (2019)
- [Bayesian Neural Networks: An Introduction and Survey](https://mlsanity.com/paper/2006.12024/) (2020)
- [Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning](https://mlsanity.com/paper/1611.03531/) (2016)
- [Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations](https://mlsanity.com/paper/1912.09007/) (2019)
- [Reinforcement Learning for Optimization of COVID-19 Mitigation policies](https://mlsanity.com/paper/2010.10560/) (2020)
- [Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment](https://mlsanity.com/paper/2101.08477/) (2021)
- [EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models](https://mlsanity.com/paper/2010.04452/) (2020)
- [Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning](https://mlsanity.com/paper/2003.14093/) (2020)
- [Reinforced Epidemic Control: Saving Both Lives and Economy](https://mlsanity.com/paper/2008.01257/) (2020)
- [VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning](https://mlsanity.com/paper/2009.06602/) (2020)
- [Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition](https://mlsanity.com/paper/2109.07827/) (2021)
- [TyXe: Pyro-based Bayesian neural nets for Pytorch](https://mlsanity.com/paper/2110.00276/) (2021)
- [A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.06589/) (2021)

## 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.