Direct Policy Iteration with Demonstrations - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

Direct Policy Iteration with Demonstrations


We consider the problem of learning the optimal policy of an unknown Markov decision process (MDP) when expert demonstrations are available along with interaction samples. We build on classification-based policy iteration to perform a seamless integration of interaction and expert data, thus obtaining an algorithm which can benefit from both sources of information at the same time. Furthermore , we provide a full theoretical analysis of the performance across iterations providing insights on how the algorithm works. Finally, we report an empirical evaluation of the algorithm and a comparison with the state-of-the-art algorithms.
Fichier principal
Vignette du fichier
DPID_CameraReady.pdf (337.12 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01237659 , version 1 (03-12-2015)


  • HAL Id : hal-01237659 , version 1


Jessica Chemali, Alessandro Lazaric. Direct Policy Iteration with Demonstrations. IJCAI - 24th International Joint Conference on Artificial Intelligence, Jul 2015, Buenos Aires, Argentina. ⟨hal-01237659⟩
224 View
356 Download


Gmail Facebook Twitter LinkedIn More