Recent advances in reinforcement learning (RL) have predominantly leveraged neural network-based policies for decision-making, yet these models often lack interpretability, posing challenges for stakeholder comprehension and trust. Concept bottleneck models offer an interpretable alternative by integrating human-understandable concepts into neural networks. However, a significant limitation in prior work is the assumption that human annotations for these concepts are readily available during training, necessitating continuous real-time input from human annotators. To overcome this limitation, we introduce a novel training scheme that enables RL algorithms to efficiently learn a concept-based policy by only querying humans to label a small set of data, or in the extreme case, without any human labels. Our algorithm, LICORICE, involves three main contributions: interleaving concept learning and RL training, using a concept ensembles to actively select informative data points for labeling, and decorrelating the concept data with a simple strategy. We show how LICORICE reduces manual labeling efforts to to 500 or fewer concept labels in three environments. Finally, we present an initial study to explore how we can use powerful vision-language models to infer concepts from raw visual inputs without explicit labels at minimal cost to performance.

The Internet of Things (IoT) is good for business optimization, but could be bad for candy lovers. Twizzlers and eventually other Hershey's candies, such as Reese's Peanut Butter Cups, are going to get smaller now that Hershey is adding IoT sensors to its candy-making manufacturing facilities to make the process more efficient through machine learning. The process to start using machine learning and predictive analytics at Hershey, which makes Twizzlers and 79 other brands of candy, began on a single Twizzlers factory line. Hershey used Microsoft Azure to utilize algorithms to improve the manufacturing process. "We were able to utilize the precooked algorithms inside of Azure to wire up all of the machine learning. We literally were able to build this without a data scientist," said George Lenhart, senior manager, advanced productivity and collaboration, at Industry of Things World USA in San Diego, Calif.