  Rapid urbanization, increasing integration of distributed renewable energy
resources, energy storage, and electric vehicles introduce new challenges for
the power grid. In the US, buildings represent about 70% of the total
electricity demand and demand response has the potential for reducing peaks of
electricity by about 20%. Unlocking this potential requires control systems
that operate on distributed systems, ideally data-driven and model-free. For
this, reinforcement learning (RL) algorithms have gained increased interest in
the past years. However, research in RL for demand response has been lacking
the level of standardization that propelled the enormous progress in RL
research in the computer science community. To remedy this, we created
CityLearn, an OpenAI Gym Environment which allows researchers to implement,
share, replicate, and compare their implementations of RL for demand response.
Here, we discuss this environment and The CityLearn Challenge, a RL competition
we organized to propel further progress in this field.
