Planning with Abstract Learned Models While Learning Transferable Subtasks

no code implementations • 16 Dec 2019 • John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shane Parr, Shawn Squire, Marie desJardins, Cynthia Matuszek

We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction.

Hierarchical Reinforcement Learning reinforcement-learning +1