mlsanityData to 14 Oct 2026

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

arXiv 2510.11283 ↗First posted 13 Oct 2025cs.LGcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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).

Key facts. Cited by 1 paper 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.

Builds on

4 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms29 Jan

Other references · 3

  1. 2021Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systemscited by 18
  2. 2020A Deep Reinforcement Learning Framework for Continuous Intraday Market Biddingcited by 15
  3. 2021Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and educationcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Controlcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2026Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark
  2. 2026Offline Reinforcement Learning for Rotation Profile Control in Tokamaks
  3. 2025Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV
  4. 2024Active Disruption Avoidance and Trajectory Design for Tokamak Ramp-downs with Neural Differential Equations and Reinforcement Learning
  5. 2025Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
  6. 2026Dynamic Plasma Shape Control with Arbitrary Sensor Subsets ↗
  7. 2025Enabling Integrated AI Control on DIII-D: A Control System Design with State-of-the-art Experiments
  8. 2026Advantage-level Aggregation Reinforcement Learning for X-point Target Magnetic Configuration Control in an EXL-50U Experiment-Calibrated Simulation Environment