In this paper we improve learning performance of a risk-aware robot facing navigation tasks by employing transfer learning; that is, we use information from a previously solved task to accelerate learning in a new task. To do so, we transfer risk-aware memoryless stochastic abstract policies into a new task. We show how to incorporate risk-awareness into robotic navigation tasks, in particular when tasks are modeled as stochastic shortest path problems. We then show how to use a modified policy iteration algorithm, called AbsProb-PI, to obtain risk-neutral and risk-prone memoryless stochastic abstract policies. Finally, we propose a method that combines abstract policies, and show how to use the combined policy in a new navigation task. Experiments validate our proposals and show that one can find effective abstract policies that can improve robot behavior in navigation problems. © 2014 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Da Silva, V. F., Koga, M. L., Cozman, F. G., & Costa, A. H. R. (2014). Reusing risk-aware stochastic abstract policies in robotic navigation learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8371 LNAI, pp. 256–267). Springer Verlag. https://doi.org/10.1007/978-3-662-44468-9_23
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