Guillermo Infante

Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes

We present a novel approach to hierarchical reinforcement learning for linearly-solvable Markov decision processes. Our approach assumes that the state space is partitioned, and defines subtasks for moving between the partitions. We represent value functions on several levels of abstraction, and use…