TempoRL: Learning When to Act

verfasst von

André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer

Abstract

Reinforcement learning is a powerful approach to learn behaviour through interactions with an environment. However, behaviours are usually learned in a purely reactive fashion, where an appropriate action is selected based on an observation. In this form, it is challenging to learn when it is necessary to execute new decisions. This makes learning inefficient, especially in environments that need various degrees of fine and coarse control. To address this, we propose a proactive setting in which the agent not only selects an action in a state but also for how long to commit to that action. Our TempoRL approach introduces skip connections between states and learns a skip-policy for repeating the same action along these skips. We demonstrate the effectiveness of TempoRL on a variety of traditional and deep RL environments, showing that our approach is capable of learning successful policies up to an order of magnitude faster than vanilla Q-learning.

Organisationseinheit(en)

Fachgebiet Maschinelles Lernen
Institut für Informationsverarbeitung

Externe Organisation(en)

Albert-Ludwigs-Universität Freiburg
Bosch Center for Artificial Intelligence (BCAI)

Typ

Aufsatz in Konferenzband

Anzahl der Seiten

18

Publikationsdatum

2021

Publikationsstatus

Elektronisch veröffentlicht (E-Pub)

Peer-reviewed

Ja

Elektronische Version(en)

https://arxiv.org/abs/2106.05262 (Zugang: Offen)
http://proceedings.mlr.press/v139/biedenkapp21a/biedenkapp21a.pdf (Zugang: Offen)