Reinforcement Learning: From AlphaGo to AlphaGo Zero

This article provides a comprehensive overview of reinforcement learning (RL), starting with the captivating story of AlphaGo defeating human Go champions. It explains core RL concepts like MDPs, Bellman equations, dynamic programming, Monte Carlo methods, TD learning (SARSA, Q-learning, DQN), policy gradient methods (REINFORCE, Actor-Critic, A3C), and evolutionary strategies. The article delves into the details of each algorithm, using AlphaGo Zero as a compelling case study to illustrate RL's practical applications and its power in solving complex problems.