Regret Minimization in MDPs with Options without Prior Knowledge - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Regret Minimization in MDPs with Options without Prior Knowledge

Résumé

The option framework integrates temporal abstraction into the reinforcement learning model through the introduction of macro-actions (i.e., options). Recent works leveraged the mapping of Markov decision processes (MDPs) with options to semi-MDPs (SMDPs) and introduced SMDP-versions of exploration-exploitation algorithms (e.g., RMAX-SMDP and UCRL-SMDP) to analyze the impact of options on the learning performance. Nonetheless, the PAC-SMDP sample complexity of RMAX-SMDP can hardly be translated into equivalent PAC-MDP theoretical guarantees, while the regret analysis of UCRL-SMDP requires prior knowledge of the distributions of the cumulative reward and duration of each option, which are hardly available in practice. In this paper, we remove this limitation by combining the SMDP view together with the inner Markov structure of options into a novel algorithm whose regret performance matches UCRL-SMDP's up to an additive regret term. We show scenarios where this term is negligible and the advantage of temporal abstraction is preserved. We also report preliminary empirical results supporting the theoretical findings.
Fichier principal
Vignette du fichier
supplementary.pdf (1.67 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01649082 , version 1 (27-11-2017)

Identifiants

  • HAL Id : hal-01649082 , version 1

Citer

Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill. Regret Minimization in MDPs with Options without Prior Knowledge. NIPS 2017 - Neural Information Processing Systems, Dec 2017, Long Beach, United States. pp.1-36. ⟨hal-01649082⟩
370 Consultations
174 Téléchargements

Partager

Gmail Facebook X LinkedIn More