Gianmarco Tedeschi

Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification

Anders Jonsson, Emilie Kaufmann, Gianmarco Tedeschi, Lorenzo Steccanella

We present HBPI-UCRL, a model-based algorithm for hierarchical reinforcement learning (HRL) that learns high-level and low-level policies in parallel. HBPI-UCRL exploits the fact that a high-level transition corresponds to a multi-step transition at the low level. We introduce tw…Read more