Caught Red-Bandit

A multi-armed bandit problem is a problem where there are multiple actions you can take and each action gives some reward, creating questions around whether to explore or exploit. We created a multi-armed bandit gym environment to help people understand how a transformer makes strategic decisions in such a scenario. While previous transformer interpretability research has focused on how models understand language, our solution is novel in the sense that it looks into the model’s strategic decision making process.