glucosim 0.1.1

Gymnasium environments for reinforcement learning in glucose management

glucosim

Gymnasium Environments for Reinforcement Learning in Glucose Management

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Three Gymnasium-compatible RL environments for Type 1 diabetes glucose management: basal rate optimization, meal bolus dosing, and full closed-loop insulin delivery control. Includes the Bergman minimal glucose-insulin model, Dalla Man gut absorption dynamics, a CGM sensor noise model, 30 virtual patients across three age groups (child, adolescent, adult), configurable difficulty tiers, heuristic clinical baselines, PPO RL agents, and a five-tier benchmark suite. 120 tests, MIT licensed.

Installation

pip install glucosim              # Core (numpy, scipy, gymnasium)
pip install glucosim[train]       # + SB3, PyTorch for RL training
pip install glucosim[all]         # Everything

Development install:

git clone https://github.com/HassDhia/glucosim.git
cd glucosim
pip install -e ".[all]"

Quick Start

import gymnasium as gym
import glucosim

env = gym.make("glucosim/BasalControl-v0")
obs, info = env.reset(seed=42)
for _ in range(100):
    action = env.action_space.sample()
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        obs, info = env.reset()
env.close()

Environments

Environment	Paradigm	Observation	Action	Key Challenge
BasalControl-v0	Continuous basal rate	CGM glucose, IOB, time, glucose rate	Basal rate (U/hr)	Maintain euglycemia over 24h
BolusAdvisor-v0	Meal bolus dosing	CGM glucose, IOB, meal flag, carbs, time since meal	Bolus dose (U)	Optimal postprandial control
ClosedLoop-v0	Full closed-loop	CGM glucose, IOB, time, glucose rate, meal flag, carbs	Total insulin rate (U/hr)	48h stress test with IOB penalty

Architecture

GlucoSim implements a modular simulation stack:

• Bergman Minimal Model - Three-equation glucose-insulin dynamics with RK4 integration
• Dalla Man Gut Model - Two-compartment carbohydrate absorption producing glucose appearance rates
• CGM Sensor Model - First-order lag filter with configurable Gaussian noise
• Virtual Patients - 30 patients across child/adolescent/adult groups with +/-20% parameter variability
• Baseline Agents - Random, proportional basal controller, and ICR bolus calculator
• PPO Agent - Stable Baselines3 PPO with tuned hyperparameters
• Benchmark Suite - Five difficulty tiers per environment (easy-adult to hard-child)

Paper

The accompanying paper is available at:

• PDF (GitHub)

Citation

If you use glucosim in your research, please cite:

@software{dhia2026glucosim,
  author = {Dhia, Hass},
  title = {GlucoSim: Gymnasium Environments for Reinforcement Learning in Glucose Management},
  year = {2026},
  publisher = {Smart Technology Investments Research Institute},
  url = {https://github.com/HassDhia/glucosim}
}

License

MIT License. See LICENSE for details.

Contact

Hass Dhia - Smart Technology Investments Research Institute

• Email: [e-mail]
• Web: smarttechinvest.com/research