Explore the concept of "Exploration via Randomized Value Functions" in this 50-minute lecture from the 2019 ADSI Summer Workshop on Algorithmic Foundations of Learning and Control. Delve into topics such as deep exploration, linear sanity checking, stochastic policies, and Thompson sampling. Examine algorithmic ideas, intuition, and theory behind shortest path problems, episodic reinforcement learning, and model-based tabular learning. Investigate scalable approximations, value function approximations, and the real challenges facing the field. Gain insights into deep questions surrounding exploration in learning and control algorithms.