Reinforcement learning is a powerful paradigm for modeling autonomous and intelligent agents interacting with the environment, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This course provides an introduction to reinforcement learning intelligence, which focuses on the study and design of agents that interact with a complex, uncertain world to achieve a goal. We will study agents that can make near-optimal decisions in a timely manner with incomplete information and limited computational resources.

The course will cover Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information-processing approach to the concept of mind and briefly touch on perspectives from psychology, neuroscience, and philosophy.