| Abstract |
Reinforcement learning (RL) focuses on achieving efficient learning and optimal decision-making from available trials. Recent breakthroughs such as ChatGPT, robotics, autonomous driving, and recommendation systems owe much to advancements in reinforcement learning.
Reinforcement learning is often framed as the ‘exploration vs. exploitation’ dilemma. In each trial, the learning agent must decide between ‘exploring’ to discover new possible outcomes or ‘exploiting’ by choosing familiar actions that yield reliable rewards. Effective exploration is crucial to enabling the agent to understand its environment with fewer trials, thereby saving trial opportunities for exploitation, which ultimately maximizes cumulative reward.
In this talk, we will delve into a deeper understanding of efficient exploration through two RL variants: the bandit problem and best-arm identification. Throughout the series of new results, we will discuss how to address the two key aspects of exploration research: the design of experiments and the stopping condition for exploration. |
