Online Learning and Bandits - Part 2

Delve into the intricacies of online learning and bandit algorithms in this comprehensive lecture from the Theory of Reinforcement Learning Boot Camp. Explore fundamental concepts such as the basic bandit game, regret analysis, and adversarial protocols. Learn about key algorithm design principles, including exponential weights, optimism in the face of uncertainty, and probability matching. Examine popular algorithms like Exp3, UCB, and Thompson Sampling, along with their analyses and upper bounds. Investigate advanced topics such as best of both worlds scenarios, successive elimination, and linear contextual bandits. Gain insights from experts Alan Malek of DeepMind and Wouter Koolen from Centrum Wiskunde & Informatica as they guide you through this essential area of reinforcement learning theory.