Authors:
Andrea Bonarini
;
Alessandro Lazaric
and
Marcello Restelli
Affiliation:
Politecnico di Milano, Italy
Keyword(s):
Robot Learning, Reinforcement Learning.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
Abstract:
Writing good behaviors for mobile robots is a hard task that requires a lot of hand tuning and often fails to consider all the possible configurations that a robot may face. By using reinforcement learning techniques a robot can improve its performance through a direct interaction with the surrounding environment and adapt its behavior in response to some non-stationary events, thus achieving a higher degree of autonomy with respect to
pre-programmed robots. In this paper, we propose a novel reinforcement learning approach that addresses the main issues of learning in real-world robotic applications: experience is expensive, explorative actions are risky, control policy must be robust, state space is continuous. Preliminary results performed on a real robot suggest that on-line reinforcement learning, matching some specific solutions, can be effective also in real-world physical environments.