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

Bertin, A. A.; Bucher, E. E.; Griere, O. O.; Hurtado, M. M.; L. Rocha, H.; Heiland, R.; Sundus, A.; Macklin, P.; Francois-Lavet, V.; Rachelson, E.; Pancaldi, V.

doi:10.1101/2025.09.18.677030

bioRxiv · 10.1101/2025.09.18.677030

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

Bertin, A. A.·Bucher, E. E.·Griere, O. O.·Hurtado, M. M.·L. Rocha, H.·Heiland, R.·Sundus, A.·Macklin, P.·Francois-Lavet, V.·Rachelson, E.·Pancaldi, V.

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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.

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Bertin, A. A., Bucher, E. E., Griere, O. O., Hurtado, M. M., L. Rocha, H., Heiland, R., Sundus, A., Macklin, P., Francois-Lavet, V., Rachelson, E., Pancaldi, V.. 2025-09-24. PhysiGym: bridging the gap between the Gymnasium reinforcement learning application interface and the PhysiCell agent-based model software. https://doi.org/10.1101/2025.09.18.677030

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