Papers Papers/2022 Papers Papers/2022



Paper Unlock

Authors: Michael Castronovo ; Vincent François-Lavet ; Raphaël Fonteneau ; Damien Ernst and Adrien Couëtoux

Affiliation: Montefiore Institute and Universite de Liège, Belgium

Keyword(s): Bayesian Reinforcement Learning, Artificial Neural Networks, Offline Policy Search.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bayesian Networks ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: Bayesian Reinforcement Learning (BRL) agents aim to maximise the expected collected rewards obtained when interacting with an unknown Markov Decision Process (MDP) while using some prior knowledge. State-of-the-art BRL agents rely on frequent updates of the belief on the MDP, as new observations of the environment are made. This offers theoretical guarantees to converge to an optimum, but is computationally intractable, even on small-scale problems. In this paper, we present a method that circumvents this issue by training a parametric policy able to recommend an action directly from raw observations. Artificial Neural Networks (ANNs) are used to represent this policy, and are trained on the trajectories sampled from the prior. The trained model is then used online, and is able to act on the real MDP at a very low computational cost. Our new algorithm shows strong empirical performance, on a wide range of test problems, and is robust to inaccuracies of the prior distribution.


Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Castronovo, M.; François-Lavet, V.; Fonteneau, R.; Ernst, D. and Couëtoux, A. (2017). Approximate Bayes Optimal Policy Search using Neural Networks. In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-220-2; ISSN 2184-433X, SciTePress, pages 142-153. DOI: 10.5220/0006191701420153

author={Michael Castronovo. and Vincent Fran\c{C}ois{-}Lavet. and Raphaël Fonteneau. and Damien Ernst. and Adrien Couëtoux.},
title={Approximate Bayes Optimal Policy Search using Neural Networks},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},


JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Approximate Bayes Optimal Policy Search using Neural Networks
SN - 978-989-758-220-2
IS - 2184-433X
AU - Castronovo, M.
AU - François-Lavet, V.
AU - Fonteneau, R.
AU - Ernst, D.
AU - Couëtoux, A.
PY - 2017
SP - 142
EP - 153
DO - 10.5220/0006191701420153
PB - SciTePress