PhysiGym: bridging the gap between the Gymnasium reinforcement learning application interface and the PhysiCell agent-based model software
A. Bertin, Elmar Bucher, Owen OG Griere, Miguel Hurtado, Heber Lima da Rocha, Randy W. Heiland, Aneequa Sundus, Paul Macklin, Vincent François-Lavet, Emmanuel Rachelson, Véra Pancaldi · bioRxiv (Cold Spring Harbor Laboratory) · 2025
Abstract This paper presents PhysiGym, a framework that integrates agent-based biological simulation within standardized reinforcement learning environments. By integrating the agent-based modeling framework PhysiCell with the Gymnasium API, we provide a flexible tool for exploring reinforcement learning strategies to control insilico biological processes. We demonstrate PhysiGym’s potential with a case study where a deep reinforcement learning algorithm guides a tumor microenvironment model toward an anti-tumoral state, ultimately achieving tumour elimination. Our results highlight PhysiGym’s flexibility for AI-driven biological control and optimization of dynamic treatment regimes.