Title: Policy Optimization Methods for Reinforcement Learning

Abstract: Reinforcement Learning (RL) has demonstrated remarkable success across various domains, from robotics to fine-tuning large language models (LLMs). At the core of many state-of-the-art RL algorithms lies policy optimization, a class of methods that directly optimize policies to improve decision-making. This talk provides an overview of key policy optimization techniques, with a particular focus on policy gradient methods. We will explore their theoretical foundations, practical challenges, and recent advancements. Furthermore, we will discuss emerging research on policy optimization in the context of Reinforcement Learning with Human Feedback (RLHF), where learning is guided by a ranking oracle instead of explicit reward signals.