Health Gym

Health Gym is a specialized framework designed to bridge the gap between reinforcement learning (RL) research and clinical application. Originally conceived to extend the OpenAI Gym (now Gymnasium) interface into the medical domain, Health Gym provides high-fidelity, standardized environments for simulating complex physiological states. By 2026, it has become the foundational tool for 'offline-to-online' RL transitions, allowing data scientists to train agents on historical EHR (Electronic Health Record) data and validate them in simulated environments before clinical pilot phases. The technical architecture relies on Ordinary Differential Equations (ODEs) and Markov Decision Processes (MDPs) to model conditions like Sepsis and Acute Kidney Injury. Its primary utility lies in its ability to generate synthetic patient trajectories that mimic real-world physiological responses to interventions such as vasopressors, fluid resuscitation, and mechanical ventilation. As a research-centric tool, it emphasizes safety-constrained RL, ensuring that simulated policies adhere to clinical guidelines while seeking optimal patient outcomes. It is extensively used by pharmaceutical R&D and academic hospitals for policy evaluation and treatment optimization.