Gym-TORAX: Open-source software for integrating RL with plasma control simulators

Antoine Mouchamps, Arthur Malherbe, Adrien Bolland, Damien Ernst
10/13/2025
cs.LG

No official code found. Best implementation: antoine-mouchamps/gymtorax (23 stars, MIT, last commit 2026-03-19).

Abstract

This paper presents Gym-TORAX, a Python package enabling the implementation of Reinforcement Learning (RL) environments for simulating plasma dynamics and control in tokamaks. Users define succinctly a set of control actions and observations, and a control objective from which Gym-TORAX creates a Gymnasium environment that wraps TORAX for simulating the plasma dynamics. The objective is formulated through rewards depending on the simulated state of the plasma and control action to optimize specific characteristics of the plasma, such as performance and stability. The resulting environment instance is then compatible with a wide range of RL algorithms and libraries and will facilitate RL research in plasma control. In its current version, one environment is readily available, based on a ramp-up scenario of the International Thermonuclear Experimental Reactor (ITER).

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Code Implementations(1)

antoine-mouchamps/gymtoraxCommunity72%
MIT

Gym-TORAX: A software for integrating reinforcement learning with plasma control simulators

234PythonAug 11, 20257 months agoMIT

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Cite this paper

@article{mouchamps2025gymtorax,
  title  = {Gym-TORAX: Open-source software for integrating RL with plasma control simulators},
  author = {Antoine Mouchamps and Arthur Malherbe and Adrien Bolland and Damien Ernst},
  year   = {2025},
  eprint = {2510.11283},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.11283v1}
}

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