Myopic Exploration

Myopic exploration in reinforcement learning focuses on developing efficient strategies for agents to learn optimal policies in complex environments, despite only considering immediate rewards when choosing actions. Current research investigates how multitask learning, leveraging diverse training tasks or multiple expert policies, can improve the sample efficiency of these otherwise simplistic exploration methods, such as epsilon-greedy. This work aims to theoretically understand and improve the surprising practical success of myopic exploration, potentially leading to more efficient and robust reinforcement learning algorithms for real-world applications.