# Gym-TORAX: Open-source software for integrating reinforcement learning with plasma control simulators in tokamak research

arXiv: https://arxiv.org/abs/2510.11283 · HTML: https://mlsanity.com/paper/2510.11283/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 4 of 4)

- [Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems](https://mlsanity.com/paper/2103.07932/) (2021)
- [A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding](https://mlsanity.com/paper/2004.05940/) (2020)
- [Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education](https://mlsanity.com/paper/2105.08846/) (2021)
- [Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms](https://mlsanity.com/paper/2401.16321/) (2024)

## Built on by (most cited first, 1 of 1)

- [Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control](https://mlsanity.com/paper/2601.15015/) (2026)

## Abstract (arXiv)

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).