However, a related alternative-reinforcement learning-has been relatively unexplored for quantum chemical application. Whereas unsupervised learning seeks to find underlying structure in unlabeled data, and supervised learning uses labeled data to train a parameterized model, reinforcement learning is designed to learn from experiences or mistakes. It uses feedback observed from an appropriately defined environment to improve strategies to achieve some objective by taking an optimal sequence of actions. By exploring the environment that provides rewards (or penalties), the agent gradually learns which actions to take and which to avoid. In contrast to supervised learning, no answer or action is directly given to the agent, and there is no need to label data or worry about preparing training data, as all data is incorporated as it is generated in the form of a reward signal. Because data can be generated on-the-fly and is used as-is (e.g., there is no need to label data or split data into training, test, and validation sets), reinforcement learning is an attractive approach for the selected CI problem.