Anytime Learning and Adaptation of Structured Fuzzy Behaviors

Anytime Learning and Adaptation of Structured Fuzzy Behaviors (0)

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by Andrea Bonarini

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Datum	Value	Source
TITLE	1 Anytime learning and adaptation of structured fuzzy behaviors	SVM HeaderParse 0.2
AUTHOR NAME	Andrea Bonarini	SVM HeaderParse 0.2
AUTHOR AFFIL	Politecnico di Milano Artificial Intelligence and Robotics Project; Dipartimento di Elettronica e Informazione- Politecnico di Milano	SVM HeaderParse 0.2
AUTHOR ADDR	Piazza Leonardo da Vinci, 32- 20133 Milano- Italy	SVM HeaderParse 0.2
ABSTRACT	We present an approach to support effective learning and adaptation of behaviors for autonomous agents with reinforcement learning algorithms. These methods can identify control systems that optimize a reinforcement program, which is, usually, a straightforward representation of the designer's goals. Reinforcement learning algorithms are usually too slow to be applied in real time on embodied agents, although they provide a suitable way to represent the desired behavior. We have tackled three aspects of this problem: the speed of the algorithm, the learning procedure, and the control system architecture. The learning algorithm we have developed includes features to speed up learning, such as niche-based learning, and a representation of the control modules in terms of fuzzy rules that reduces the search space, and improves robustness to noisy data. Our learning procedure exploits methodologies such as learning from easy missions and transfer of policy from simpler environments to the more complex. The architecture of our control system is layered and modular, so that each module has a low complexity and it can be learned in a short time. The composition of the actions proposed by the modules is either learned or pre-defined. Finally, we adopt an anytime learning approach to improve the quality of the control system on-line and to adapt it to dynamic	SVM HeaderParse 0.2
CITATIONS	39 found	ParsCit 1.0