论文标题
低排放建筑物控制，并进行零射强化学习
Low Emission Building Control with Zero-Shot Reinforcement Learning
论文作者
论文摘要
建筑物中的加热和冷却系统占全球能源使用的31 \％，其中大部分受基于规则的控制器（RBC）调节，这些控制器（RBC）既不通过与网格最佳相互作用来最大化能源效率或最小化排放。通过强化学习（RL）的控制已被证明可以显着提高建筑物的能源效率，但是现有的解决方案需要访问世界上每个建筑物都无法期望的建筑物特定模拟器或数据。作为回应，我们表明有可能在没有这样的知识的情况下获得减少排放的政策，这是我们称为零射击建筑物控制的范式。我们结合了来自系统识别和基于模型的RL的想法，以创建珍珠（概率避免发射的增强学习），并表明建立表现模型所需的短期活跃探索是所需的。在三个不同的建筑能源模拟的实验中，我们显示珍珠的表现一次优于现有的RBC，在所有情况下，流行的RL基准均表现出色，在维持热舒适度的同时，将建筑物排放量降低了31 \％。我们的源代码可通过https://enjeener.io/projects/pearl在线获得。
Heating and cooling systems in buildings account for 31\% of global energy use, much of which are regulated by Rule Based Controllers (RBCs) that neither maximise energy efficiency nor minimise emissions by interacting optimally with the grid. Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency, but existing solutions require access to building-specific simulators or data that cannot be expected for every building in the world. In response, we show it is possible to obtain emission-reducing policies without such knowledge a priori--a paradigm we call zero-shot building control. We combine ideas from system identification and model-based RL to create PEARL (Probabilistic Emission-Abating Reinforcement Learning) and show that a short period of active exploration is all that is required to build a performant model. In experiments across three varied building energy simulations, we show PEARL outperforms an existing RBC once, and popular RL baselines in all cases, reducing building emissions by as much as 31\% whilst maintaining thermal comfort. Our source code is available online via https://enjeeneer.io/projects/pearl .
