Learning Reward Machines: A Study in Partially Observable Reinforcement Learning

no code implementations • 17 Dec 2021 • Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen, Richard Valenzano, Margarita P. Castro, Sheila A. McIlraith

Here we show that RMs can be learned from experience, instead of being specified by the user, and that the resulting problem decomposition can be used to effectively solve partially observable RL problems.

Partially Observable Reinforcement Learning Problem Decomposition +2