Tutorial on Decision Making Under Uncertainty

This tutorial led by Robert Moss, Ph.D. candidate in the Stanford Intelligent Systems Lab, covers how to build and solve sequential decision making problems in uncertain environments. Splitting the discussion between problem formulation and solution methods, this tutorial focuses on the mathematical framework for optimal sequential decision making—the Markov decision process (MDP)—and will cover online and offline solution methods (such as value iteration, Q-learning, SARSA, and Monte Carlo tree search).