  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.
