Learning To Run a Power Network (L2RPN)

该数据集为L2RPN竞赛提供标准化基准和问题表述，特别关注于电力网络控制研究中的强化学习应用。该数据集被用于在模拟的电力网络环境中训练和评估各种强化学习算法，尤其是PPO（Proximal Policy Optimization）代理。其任务是通过强化学习对电力网络拓扑进行优化。

This dataset provides standardized benchmarks and problem formulations for the L2RPN competition, with a particular focus on the application of reinforcement learning in power grid control research. It is employed to train and evaluate various reinforcement learning algorithms, especially Proximal Policy Optimization (PPO) agents, within simulated power grid environments. Its core task is to optimize power grid topologies via reinforcement learning.