PhD thesis explores robots that learn through real-world interaction

Vasan’s thesis describes three approaches to real-time reinforcement learning: ReLoD draws on remote computing, a minimum-time reward simplifies goal-reaching tasks, and AVG updates an agent after each interaction without storing past transitions. Vasan reports that physical robots learned competent behavior from scratch within hours. Sutton calls the thesis particularly well done and singles out AVG.