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 decisional 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. ©2007 IEEE.

An extended policy gradient algorithm for robot task learning / Cherubini, A; Giannone, F; Iocchi, Luca; Palamara, P. F.. - (2007), pp. 4121-4126. ((Intervento presentato al convegno 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 tenutosi a San Diego; United States nel 29 Oct / 2 Nov 2007 [10.1109/IROS.2007.4399219].

An extended policy gradient algorithm for robot task learning

IOCCHI, Luca;
2007

Abstract

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 decisional 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. ©2007 IEEE.
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
International conferences; Optimal parameters; Robot performance
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
An extended policy gradient algorithm for robot task learning / Cherubini, A; Giannone, F; Iocchi, Luca; Palamara, P. F.. - (2007), pp. 4121-4126. ((Intervento presentato al convegno 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 tenutosi a San Diego; United States nel 29 Oct / 2 Nov 2007 [10.1109/IROS.2007.4399219].
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