深入淺出近端策略優化 (PPO)：Hugging Face 深度強化學習教程
Original: Proximal Policy Optimization (PPO)
Proximal Policy Optimization (PPO) is a deep reinforcement learning (DRL) algorithm proposed by OpenAI in 2017. Due to its ease of…
本教學為 Hugging Face 深度強化學習課程的一部分，深入探討當前最主流的強化學習演算法「近端策略優化 (PPO)」。文章解析了 PPO 如何透過「剪裁代理目標函數」解決傳統策略梯度法步長過大導致崩潰的問題，並引導讀者使用 Stable-Baselines3 進行實戰演練，是理解 LLM 對齊技術（如 RLHF）的重要基石。
Proximal Policy Optimization (PPO) is a deep reinforcement learning (DRL) algorithm proposed by OpenAI in 2017. Due to its ease of implementation, training stability, and strong performance, PPO has become one of the most popular and widely used reinforcement learning algorithms today, and is also the core technique behind Reinforcement Learning from Human Feedback (RLHF) alignment for large language models (LLMs) in recent years.
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