论文标题
强化学习中的自然政策梯度解释了
Natural Policy Gradients In Reinforcement Learning Explained
论文作者
论文摘要
传统的政策梯度方法从根本上存在缺陷。自然梯度更快，更好地融合，构成了当代强化学习的基础，例如信任区域政策优化（TRPO）和近端政策优化（PPO）。本讲座的旨在阐明自然政策梯度背后的直觉，重点是思考过程和关键的数学结构。
Traditional policy gradient methods are fundamentally flawed. Natural gradients converge quicker and better, forming the foundation of contemporary Reinforcement Learning such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). This lecture note aims to clarify the intuition behind natural policy gradients, focusing on the thought process and the key mathematical constructs.
