Reinforcement Learning 103: Actor-Critic Explained (Why PPO Works)

Reinforcement Learning 103: Actor-Critic Methods In this lecture, we cover one of the most important ideas in modern reinforcement learning: 👉 Actor-Critic — combining value-based and policy-based methods 🎯 What you’ll learn: Why value-based (DQN) and policy-based methods both have limitations The intuition behind Actor (policy) + Critic (value) What advantage means (better than expected) How Actor-Critic stabilizes learning Why PPO dominates modern RL Code intuition (how this actually looks in practice) 🧠 Big Idea: Act → Evaluate → Improve 📚 This is part of my RL series: RL 101: Foundations (MDP, policy, value) RL 102: Value vs Policy RL 103: Actor-Critic (this video) 🚀 Research Opportunities (Open for Collaboration) I’m currently working on RL research projects (survey + experiments), open to students and collaborators: 👉 Q-learning survey: https://drive.google.com/file/d/1jfqbMKMKoUyUjdEYMBJg2UIiA5ZnzG5e/view 👉 RL survey (Project Q): https://drive.google.com/file/d/1cZeZicil8ZRn4C7eqGsoMz9hLcMvsje4/view No prior research experience needed — just curiosity! 🌍 Community Vision We’re building a learning community where: anyone can learn anyone can share anyone can teach If you’re interested, feel free to reach out. 📌 Follow for more: YouTube: @Colby豆布斯 Bilibili: @colby豆布斯