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

Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response