We explore a computational approach to learning from interaction.

We explore idealized learning situations and evaluate the effectiveness of various learning methods.

We explore designs for machines that are effective in solving learning problems of scientific or economic interest, evaluating the designs through mathematical analysis or computational experiments.

Reinforcement learning problems involve learning what to do—how to map situations to actions—so as to maximize a numerical reward signal. There are three characteristics:

• being closed-loop in an essential way
• not having direct instructions as to what actions to take
• where the consequences of actions, including reward signals, play out over extended time periods