abx_amr_simulator: A simulation environment for antibiotic prescribing policy optimization under antimicrobial resistance

arXiv cs.LG / 3/13/2026

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Key Points

Introduces abx_amr_simulator, a Python-based, reinforcement learning (RL)-compatible environment to model antibiotic prescribing and AMR dynamics in a controlled setting.
Features modular patient attributes, antibiotic-specific AMR response curves, and a leaky-balloon abstraction to represent resistance dynamics for customizable stewardship experiments.
Learners can explore partial observability with noise, bias, and delays, and the package is compatible with the Gymnasium RL API to train and test RL agents across diverse clinical scenarios.
Provides a configurable benchmark for sequential decision-making under uncertainty, enabling researchers to study AMR dynamics and optimize antibiotic stewardship strategies.
Allows balancing immediate clinical benefits against long-term resistance management through reward function customization, supporting policy optimization in realistic, uncertain environments.

Abstract

Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibiotics and complicating clinical decision-making. To address this challenge, we introduce abx_amr_simulator, a Python-based simulation package designed to model antibiotic prescribing and AMR dynamics within a controlled, reinforcement learning (RL)-compatible environment. The simulator allows users to specify patient populations, antibiotic-specific AMR response curves, and reward functions that balance immedi- ate clinical benefit against long-term resistance management. Key features include a modular design for configuring patient attributes, antibiotic resistance dynamics modeled via a leaky-balloon abstraction, and tools to explore partial observability through noise, bias, and delay in observations. The package is compatible with the Gymnasium RL API, enabling users to train and test RL agents under diverse clinical scenarios. From an ML perspective, the package provides a configurable benchmark environment for sequential decision-making under uncertainty, including partial observability induced by noisy, biased, and delayed observations. By providing a customizable and extensible framework, abx_amr_simulator offers a valuable tool for studying AMR dynamics and optimizing antibiotic stewardship strategies under realistic uncertainty.