  Reinforcement learning is a promising model-free and adaptive controller for
demand side management, as part of the future smart grid, at the district
level. This paper presents the results of the algorithm that was submitted for
the CityLearn Challenge, which was hosted in early 2020 with the aim of
designing and tuning a reinforcement learning agent to flatten and smooth the
aggregated curve of electrical demand of a district of diverse buildings. The
proposed solution secured second place in the challenge using a centralised
'Soft Actor Critic' deep reinforcement learning agent that was able to handle
continuous action spaces. The controller was able to achieve an averaged score
of 0.967 on the challenge dataset comprising of different buildings and
climates. This highlights the potential application of deep reinforcement
learning as a plug-and-play style controller, that is capable of handling
different climates and a heterogenous building stock, for district demand side
management of buildings.
