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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 examine how offlinetraining can be leveraged to minimize data costs (accelerate convergence) andprogram implementation costs. We present two approaches to doing so:pretraining our model to warm start the experiment with simulated tasks, andusing a planning model trained to simulate the real world's rewards to theagent. We present results that demonstrate the utility of offline reinforcementlearning to efficient price-setting in the energy demand response problem.

Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs