This series on reinforcement learning was guest-authored by Misha Laskin while he was at UC Berkeley. Misha's focus areas are unsupervised learning and reinforcement learning. Reinforcement learning (RL) has played a critical role in the rapid pace of AI advances over the last decade, allowing algorithms to both interact with the world and reason over periods of time to achieve goals. RL algorithms work similarly to training dogs to learn new tricks, receiving numerical rewards for desired actions, but face challenges such as the exploration problem, where they must balance finding optimal solutions with exploring their environment. The ideas that have shaped modern RL were formalized in 1989 by Chris Watkins and later popularized by breakthroughs like AlexNet and DeepMind's Deep Q Network (DQN), which paved the way for advances in image classification, video game playing, and other areas. Today, RL is being applied to solve a variety of practical problems, including recommender systems, autonomous vehicle navigation, and data center cooling, with algorithms tailored to specific applications like SlateQ for product recommendations and Google's new algorithm addressing implicit bias.