In which scenario is reinforcement learning a better option than supervised learning?

Reinforcement learning is particularly suited for scenarios where simulation trial and error is possible because it operates based on the concept of agents interacting with an environment. In such settings, the agent learns to make decisions by receiving feedback in the form of rewards or penalties based on its actions. This trial-and-error approach allows the agent to explore different strategies and find optimal solutions over time, which is especially powerful in complex environments where the outcomes of actions may not be immediately clear.

In contrast, scenarios utilizing scarce labeled data or requiring clear labeled outcomes are typically better addressed by supervised learning, which relies heavily on labeled datasets for training. Furthermore, when the interpretability of a model is crucial, such as in high-stakes decision-making, less complex and more transparent models like those in supervised learning might be preferred. Reinforcement learning thrives in environments where feedback can be gathered iteratively through interactions, making it the better option in cases where such simulation-driven exploration can occur.