用RL控制核聚变等离子体，开源工具让研究更高效。
Gym-TORAX: Open-source software for integrating reinforcement learning with plasma control simulators in tokamak research
- 通过简洁定义动作与观测，自动生成RL环境。
- 基于ITER的升压场景，实现等离子体稳定与性能优化。
- 兼容主流强化学习框架，适合等离子体控制研究者使用。
本文提出Gym-TORAX，一个Python工具包，可将强化学习（RL）环境集成至托卡马克等离子体模拟器中。用户只需定义控制动作、观测变量和目标函数，Gym-TORAX即生成适配TORAX模拟器的Gymnasium环境，用于模拟等离子体动态并优化其性能与稳定性。奖励函数基于等离子体状态与控制动作设计，目标是提升关键指标。当前版本已提供基于国际热核聚变实验堆（ITER）升压场景的现成环境，支持多种主流强化学习算法与库，推动等离子体控制领域的智能算法研究。
原文摘要 · Abstract (English)
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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