Improving the learning process of deep reinforcement learning agents operating in collective heating environments

Deep reinforcement learning (DRL) can be used to optimise the performance of Collective Heating Systems (CHS) by reducing operational costs while ensuring thermal comfort. However, heating systems often exhibit slow responsiveness to control inputs due to thermal inertia, which delays the effects of actions such as adapting temperature set points. This delayed feedback complicates the learning process for DRL agents, as it becomes more difficult to associate specific control actions with their outcomes. To address this challenge, this study evaluates four hyperparameter schemes during training. The focus lies on schemes with varying learning rate (the rate at which weights in neural networks are adapted) and/or discount factor (the importance the DRL agent attaches to future rewards). In this respect, we introduce the GALER approach, which combines the progressive increase of the discount factor with the reduction of the learning rate throughout the training process. The effectiveness of the four learning schemes is evaluated using the actor-critic Proximal Policy Optimization (PPO) algorithm for three types of CHS with a multi-objective reward function balancing thermal comfort and energy use or operational costs. The results demonstrate that energy-based reward functions allow for limited optimisation possibilities, while the GALER scheme yields the highest potential for price-based optimisation across all considered concepts. It achieved a 3%–15% performance improvement over other successful training schemes. DRL agents trained with GALER schemes strategically anticipate on high-price times by lowering the supply temperature and vice versa. This research highlights the advantage of varying both learning rates and discount factors when training DRL agents to operate in complex multi-objective environments with slow responsiveness.