为电力系统强化学习设计标准化测试基准，提升算法可靠性与实用性。
RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations
- 基于真实电网仿真构建统一评估框架，规范状态、动作与奖励
- 引入专家经验设计安全约束，确保算法符合物理规律
- 提供基线性能指标，推动更可靠的实际系统应用研究
强化学习（RL）可为电力系统去碳化提供自适应、可扩展的控制策略，但其在复杂动态、长时程目标及严格物理约束下表现受限。为此，我们与电力系统运营商合作推出RL2Grid基准，基于RTE法国的电网仿真框架，统一任务定义、状态与动作空间及奖励机制，实现对各类强化学习算法的系统性评估与对比。同时，融合运行经验设计安全约束，确保算法输出符合实际物理要求。通过建立经典RL基线在该基准上的参考性能，揭示现有方法在真实系统中的不足，并探讨未来基于强化学习的电网控制发展方向。
原文摘要 · Abstract (English)
Reinforcement learning (RL) can provide adaptive and scalable controllers essential for power grid decarbonization. However, RL methods struggle with power grids' complex dynamics, long-horizon goals, and hard physical constraints. For these reasons, we present RL2Grid, a benchmark designed in collaboration with power system operators to accelerate progress in grid control and foster RL maturity. Built on RTE France's power simulation framework, RL2Grid standardizes tasks, state and action spaces, and reward structures for a systematic evaluation and comparison of RL algorithms. Moreover, we integrate operational heuristics and design safety constraints based on human expertise to ensure alignment with physical requirements. By establishing reference performance metrics for classic RL baselines on RL2Grid's tasks, we highlight the need for novel methods capable of handling real systems and discuss future directions for RL-based grid control.
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