antibiosim 0.1.0

Gymnasium environments for reinforcement learning in antimicrobial stewardship and antibiotic treatment optimization

AntibioSim

Gymnasium environments for reinforcement learning in antimicrobial stewardship and antibiotic treatment optimization.

AntibioSim provides four Gymnasium-compatible environments that model key clinical decision points in antibiotic therapy: drug selection, dose optimization, therapy switching, and ward-level resistance control. Each environment is grounded in established pharmacokinetic/pharmacodynamic (PK/PD) models and bacterial dynamics from the antimicrobial stewardship literature.

Environments

Environment	Task	Action Space	Difficulty
AntibioticSelection-v0	Choose optimal antibiotic from formulary	Discrete(5)	Easy
DoseOptimization-v0	Optimize dosing for PK/PD target attainment	Box(1)	Medium
TherapySwitch-v0	IV-to-oral switch and escalation decisions	Discrete(4)	Medium
ResistanceControl-v0	Ward-level antibiotic policy for resistance control	MultiDiscrete(3^5)	Hard

Installation

pip install antibiosim

For training:

pip install antibiosim[train]

Quick Start

import gymnasium as gym
import antibiosim

env = gym.make("antibiosim/AntibioticSelection-v0")
obs, info = env.reset()

for _ in range(14):
    action = env.action_space.sample()
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        break

Domain Models

• Pharmacokinetics: One-compartment and two-compartment PK models (Drusano, 2004)
• Pharmacodynamics: Emax and sigmoidal Emax models (Regoes et al., 2004)
• Bacterial dynamics: Logistic growth with antibiotic kill (Austin et al., 1999)
• Resistance: Two-population susceptible/resistant dynamics (Levin & Bonten, 2004)

Baseline Agents

• Random: Uniform random action selection
• Heuristic: Clinical guideline-based rules (Barlam et al., 2016)
• PPO: Proximal Policy Optimization via Stable-Baselines3

Training

# Train PPO on all environments
python -m antibiosim.training.train_all

# Train a single environment
antibiosim-train --env antibiosim/DoseOptimization-v0

Citation

@software{dhia2026antibiosim,
  author = {Dhia, Hass},
  title = {AntibioSim: Gymnasium Environments for Reinforcement Learning in Antimicrobial Stewardship},
  year = {2026},
  url = {https://github.com/HassDhia/antibiosim},
  version = {0.1.0}
}

License

MIT License. Copyright (c) 2026 Hass Dhia, Smart Technology Investments Research Institute.