论文标题
Andes_Gym：电力系统中深入增强学习的多功能环境
Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power Systems
论文作者
论文摘要
本文介绍了Andes_gym，这是一种用于电力系统研究的多功能且高性能的增强学习环境。环境利用安第斯山脉的建模和仿真能力以及加固学习（RL）环境OpenAI健身房，以实现Power Systems的RL算法的原型和演示。详细阐述了所提出的软件工具的架构，以提供RL算法的观察和操作接口。一个示例显示了基于可用算法训练的RL迅速原型的负载频率控制算法。通过支持安第斯山脉中可用的所有电力系统动态模型以及可用于OpenAI体育馆的大量RL算法，提出的环境可以高度推广。
This paper presents Andes_gym, a versatile and high-performance reinforcement learning environment for power system studies. The environment leverages the modeling and simulation capability of ANDES and the reinforcement learning (RL) environment OpenAI Gym to enable the prototyping and demonstration of RL algorithms for power systems. The architecture of the proposed software tool is elaborated to provide the observation and action interfaces for RL algorithms. An example is shown to rapidly prototype a load-frequency control algorithm based on RL trained by available algorithms. The proposed environment is highly generalized by supporting all the power system dynamic models available in ANDES and numerous RL algorithms available for OpenAI Gym.
