Autonomous Hierarchical Skill Acquisition in Factored MDPs

by Christopher M. Vigorito , Andrew G. Barto

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Datum	Value	Source
TITLE	Autonomous Hierarchical Skill Acquisition in Factored MDPs	SVM HeaderParse 0.2
AUTHOR NAME	Christopher M. Vigorito	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science; University of Massachusetts Amherst	SVM HeaderParse 0.2
AUTHOR ADDR	Amherst, MA 01002	SVM HeaderParse 0.2
AUTHOR NAME	Andrew G. Barto	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science; University of Massachusetts Amherst	SVM HeaderParse 0.2
AUTHOR ADDR	Amherst, MA 01002	SVM HeaderParse 0.2
ABSTRACT	Abstract — Learning hierarchies of reusable skills is essential for efficiently solving multiple tasks in a given domain. Understanding the causal relationships between one’s actions and various dimensions of one’s environment can facilitate learning of abstract skills that may be used subsequently in related tasks. Using Bayesian network structure-learning techniques and structured dynamic programming algorithms, we show that reinforcement learning agents can learn incrementally and autonomously both the causal structure of their environment and useful skills that exploit this structure. As new structure is discovered, more complex skills are learned, which in turn allow the agent to discover more structure, and so on. Because of this bootstrapping property, our approach can be considered a developmental process that results in steadily increasing domain knowledge and behavioral complexity. I.	SVM HeaderParse 0.2
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