An extended policy gradient algorithm for robot task learning - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2007

An extended policy gradient algorithm for robot task learning

Résumé

In real-world robotic applications, many factors, both at low-level (e.g., vision and motion control parameters) and at high-level (e.g., the behaviors) determine the quality of the robot performance. Thus, for many tasks, robots require fine tuning of the parameters, in the implementation of behaviors and basic control actions, as well as in strategic deci-sional processes. In recent years, machine learning techniques have been used to find optimal parameter sets for different behaviors. However, a drawback of learning techniques is time consumption: in practical applications, methods designed for physical robots must be effective with small amounts of data. In this paper, we present a method for concurrent learning of best strategy and optimal parameters, by extending the policy gradient reinforcement learning algorithm. The results of our experimental work in a simulated environment and on a real robot show a very high convergence rate.
Fichier principal
Vignette du fichier
C8-extendedPG-IROS.pdf (1.29 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01247244 , version 1 (21-12-2015)

Identifiants

Citer

Andrea Cherubini, F Giannone, L Iocchi, Pier Francesco Palamara. An extended policy gradient algorithm for robot task learning. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS'07, Oct 2007, San Diego, United States. ⟨10.1109/IROS.2007.4399219⟩. ⟨hal-01247244⟩
48 Consultations
87 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More