  Classical methods to control heating systems are often marred by suboptimal
performance, inability to adapt to dynamic conditions and unreasonable
assumptions e.g. existence of building models. This paper presents a novel deep
reinforcement learning algorithm which can control space heating in buildings
in a computationally efficient manner, and benchmarks it against other known
techniques. The proposed algorithm outperforms rule based control by between
5-10% in a simulation environment for a number of price signals. We conclude
that, while not optimal, the proposed algorithm offers additional practical
advantages such as faster computation times and increased robustness to
non-stationarities in building dynamics.
