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Through many recent successes in simulation and real-world projects, reinforcement learning (RL) has emerged as a promising approach for demand response applications especially in the residential setting. Reinforcement learning is a self-learning and self-adaptive technique that can be used to control flexibility providing devices by relying mainly on historical and/or real-time data rather than on system models. This paper presents a benchmark of five RL algorithms - fitted Q-iteration, policy iteration with Q-functions, double Q-learning, REINFORCE and actor-critic - and compares these with a model-based optimal, rule-based and naive control. We consider a task of controlling the operation of a heat pump (HP) for space heating in a building with a photovoltaic (PV) installation. The HP is controlled with goal of maximizing PV self-consumption and consequently, minimizing electricity cost. To evaluate the …