He past few years have seen tremendous progress in reinforcement
learning (RL). From complex games to robotic object manipulation, RL has
qualitatively advanced the state of the art. However, modern RL techniques
require a lot for success: a largely deterministic stationary environment, an
accurate resettable simulator in which mistakes and especially their
consequences are limited to the virtual sphere, powerful computers, and a lot
of energy to run them. At Microsoft Research, we are working towards automatic
decision-making approaches that bring us closer to the vision of AI agents
capable of learning and acting autonomously in changeable open-world conditions
using the limited onboard compute. Project Frigatebird is our ambitious quest in
this space, aimed at building intelligence that can enable small fixed-wing
uninhabited aerial vehicles (sUAVs) to stay aloft purely by extracting energy
from moving air.
Thanks to Gareth Hayter.