How does the exploration-exploitation dilemma manifest in the multi-armed bandit problem, and what are the key challenges in balancing exploration and exploitation in more complex environments?
The exploration-exploitation dilemma is a fundamental challenge in the field of reinforcement learning (RL), particularly exemplified in the multi-armed bandit problem. This dilemma involves the decision-making process where an agent must choose between exploring new actions to discover their potential rewards (exploration) and exploiting known actions that have yielded high rewards in the past (exploitation).
Explain the concept of regret in reinforcement learning and how it is used to evaluate the performance of an algorithm.
In the domain of reinforcement learning (RL), the concept of "regret" is integral to understanding and evaluating the performance of algorithms, particularly in the context of the tradeoff between exploration and exploitation. Regret quantifies the difference in performance between an optimal strategy and the strategy employed by the learning algorithm. This metric helps in assessing