PhysiGym: bridging the gap between the Gymnasium reinforcement learning application interface and the PhysiCell agent-based model software

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 PhysiGyms 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 PhysiGyms flexibility for AI-driven biological control and optimization of dynamic treatment regimes.