Lecture
This lecture introduces the concept of multi-arm bandits, a framework in reinforcement learning where an agent interacts with an environment by choosing actions to explore and exploit. The instructor explains the exploration-exploitation trade-off, the notion of regret, and the strategy of sampling from different arms to estimate their means. The goal is to minimize regret by balancing between exploring new actions and exploiting the best-known action. The lecture covers the exploration phase, empirical mean estimation, and the challenge of determining the optimal strategy in a dynamic environment.