Privileged observations enable rapid and reliable policy discovery directly in the physical world
TL;DR
This paper studies how privileged information about a physical system affects the discovery of high-performing policies when training a reinforcement learning agent directly in the physical world, and proves that privileged observations can be decisive for policy discovery even when the resulting actions can be replayed in open loop.
Abstract
We study how privileged information about a physical system affects the discovery of high-performing policies when training a reinforcement learning agent directly in the physical world. We let the agent control a cylinder in a tabletop water channel to maximize or minimize drag. The flow is chaotic and difficult to model or simulate accurately and good strategies are not obvious beforehand. Decades-old experimental studies provide recipes for simple, high-performance, periodic open-loop policies that increase drag by 26.6% +/- 0.7% and reduce it by 29.7% +/- 1.3%. With dense observations of the cylinder wake, the agent learns within tens of minutes policies that increase drag by 25.5% +/- 0.9% and reduce it by 32.4% +/- 1.6%. We record action trajectories during online policy execution and replay them open loop as fixed sequences; these replays increase drag by 23.2% +/- 2.2% and reduce it by 32.1% +/- 3.0%. However, when we withhold flow observations during training, the agent still learns to decrease drag by 31.4% +/- 2.1%, but no run learns to increase it (1.8% +/- 4.3% drag increase). Our physical experiments demonstrate an extreme case: privileged observations can be decisive for policy discovery even when the resulting actions can be replayed in open loop.
