Tag: reinforcement-learning
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RL Basics: MDP to Q-Learning in 5 Diagrams
Learn MDP fundamentals and Q-learning algorithms through 5 clear diagrams. Build intuition for reinforcement learning core concepts step-by-step.
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SAC: The Best Algorithm for Continuous Control
Implement Soft Actor-Critic from scratch โ maximum entropy RL, twin Q-networks, automatic temperature tuning, and when to choose SAC over PPO.
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PPO: Why It Powers ChatGPT and Game AI
Deep dive into Proximal Policy Optimization โ the clipping trick, GAE, hyperparameters, and why PPO became the backbone of RLHF in modern LLMs.
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Policy Gradient and Actor-Critic Explained
Master policy gradient methods from REINFORCE to Actor-Critic with GAE. Learn why direct policy optimization unlocks continuous action spaces.
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From Q-Learning to DQN: Your First RL Algorithms
Implement Q-Learning from scratch, then scale up to Deep Q-Networks with experience replay and target networks. Complete PyTorch code included.
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RL Fundamentals: MDP, Bellman Equation, and Value Functions
Learn the foundations of Reinforcement Learning โ MDP framework, policies, value functions, Bellman equation, and exploration strategies with hands-on Python code.
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Advanced Game AI: Multi-Agent RL and Self-Play
Build AlphaGo-style game AI: self-play, curriculum learning, and population training. Why single-agent RL fails for competitive games.