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