深度強化學習入門：優勢動作評價演算法 (Advantage Actor Critic, A2C)
Original: Advantage Actor Critic (A2C)
This is a classic unit from Hugging Face's Deep Reinforcement Learning Course, offering a deep dive into the Advantage Actor-Critic…
本教學為 Hugging Face 深度強化學習課程的一部分，詳細解析 Advantage Actor Critic (A2C) 演算法。A2C 結合了 Actor（負責決策）與 Critic（負責評估）的優勢，並透過優勢函數（Advantage Function）降低變異數，提升訓練穩定度。讀者將學習其核心數學原理，並了解如何實作與訓練 AI 代理人。
This is a classic unit from Hugging Face's Deep Reinforcement Learning Course, offering a deep dive into the Advantage Actor-Critic algorithm (A2C). In reinforcement learning, traditional policy gradient methods (such as REINFORCE) are intuitive but often suffer from high variance, making the training process highly unstable. To overcome this shortcoming, the Actor-Critic architecture emerged — combining the strengths of both policy-based and value-based approaches.
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