Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community, collecting training data for the reinforcement learning agent is costly and will be limited. In this work, we apply a meta-learning architecture to warm start the experiment with simulated tasks, to increase sample efficiency. We present results that demonstrate a similar a step up in complexity still corresponds with better learning.

翻译：我们的团队建议在一个办公大楼里进行一个全面的能源需求反应实验。 尽管这是一项令人振奋的努力,将为社区提供价值,但为强化学习机构收集培训数据成本高昂且有限。 在这项工作中,我们应用一个元学习架构来温暖模拟任务实验的开始,提高样本效率。 我们展示的结果显示,类似的复杂程度的提升仍然与更好的学习相适应。