Master Reinforcement Learning FAST in 2026! 🚀 From zero to hero: MDPs → DQN → PPO → RLHF → LLM-powered agents in just 8 minutes.
What you'll learn:
✅ RL basics: States, actions, rewards, Bellman equation
✅ Core algorithms: Monte Carlo, TD Learning, Policy Gradients
✅ Advanced: Offline RL, MCTS, Multi-Agent, Hierarchical RL
✅ 2026 frontiers: LLMs as reward designers, in-context RL, robotics + AGI
Perfect for developers & AI engineers building smart agents.
Timestamps below!
0:00 Intro to RL
0:20 RL Loop & Rewards
0:37 Markov Decision Processes (MDP)
1:26 Bellman Equation & Values
2:10 Exploration vs Exploitation
2:47 TD Learning & DQN
3:24 PPO & Policy Gradients
4:00 Model-Based (MCTS)
4:36 RLHF & LLM Renaissance
5:19 LLM Reward Design (Eureka)
5:38 In-Context RL
6:18 Multi-Agent & Hierarchical
7:17 Challenges & Future (Multimodal AGI)
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