Digital twins can transform agricultural production systems and supply chains, curbing greenhouse gas emissions, food waste and malnutrition. However, the potential of these advanced virtualization technologies is yet to be realized. Here, we consider the promise of digital twins across six typical agrifood supply chain steps and emphasize key implementation barriers.

Reinforcement learning (RL) is often considered a promising approach for controlling complex building operations. In this context, RL algorithms are typically evaluated using a testing framework that simulates building operations. To make general claims and avoid overfitting, an RL method should be evaluated on a large and diverse set of buildings. Unfortunately, due to the complexity of creating building simulations, none of the existing frameworks provide more than a handful of simulated buildings. Moreover, each framework has its own particularities, which makes it difficult to evaluate the same algorithm on multiple frameworks. To address this, we present Beobench: a Python toolkit 1 that provides unified access to building simulations from multiple frameworks using a container-based approach. We demonstrate the power of our approach with an example showing how Beobench can launch RL experiments in any supported framework with a single command.

Heating and cooling systems in buildings account for 31% of global energy use, much of which are regulated by Rule Based Controllers (RBCs) that neither maximise energy efficiency nor minimise emissions by interacting optimally with the grid. Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency, but existing solutions require access to building-specific simulators or data that cannot be expected for every building in the world. In response, we show it is possible to obtain emission-reducing policies without such knowledge a priori–a paradigm we call zero-shot building control. We combine ideas from system identification and model-based RL to create PEARL (Probabilistic Emission-Abating Reinforcement Learning) and show that a short period of active exploration is all that is required to build a performant model. In experiments across three varied building energy simulations, we show PEARL outperforms an existing RBC once, and popular RL baselines in all cases, reducing building emissions by as much as 31% whilst maintaining thermal comfort. Our source code is available online via: https://enjeeneer.io/projects/pearl/.