论文标题
CityLearn：在需求响应和城市能源管理的多机构增强学习中标准化研究
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management
论文作者
论文摘要
快速的城市化，分布式可再生能源资源，能源存储和电动汽车的整合提出了新的挑战。在美国，建筑物约占总电力需求和需求响应的70％，有可能使电力峰降低约20％。解锁该潜力需要在分布式系统上运行的控制系统，理想情况下是数据驱动和模型。为此，在过去几年中，增强学习（RL）算法越来越兴趣。但是，RL的需求响应研究缺乏标准化水平，这推动了计算机科学界RL研究的巨大进展。为了解决这一问题，我们创建了CityLearn，这是一个OpenAI健身环境，它允许研究人员实施，共享，复制和比较其对RL的实施以进行需求响应。在这里，我们讨论了这个环境和城市实验挑战赛，这是我们组织的RL竞赛，以推动该领域的进一步进展。
Rapid urbanization, increasing integration of distributed renewable energy resources, energy storage, and electric vehicles introduce new challenges for the power grid. In the US, buildings represent about 70% of the total electricity demand and demand response has the potential for reducing peaks of electricity by about 20%. Unlocking this potential requires control systems that operate on distributed systems, ideally data-driven and model-free. For this, reinforcement learning (RL) algorithms have gained increased interest in the past years. However, research in RL for demand response has been lacking the level of standardization that propelled the enormous progress in RL research in the computer science community. To remedy this, we created CityLearn, an OpenAI Gym Environment which allows researchers to implement, share, replicate, and compare their implementations of RL for demand response. Here, we discuss this environment and The CityLearn Challenge, a RL competition we organized to propel further progress in this field.
