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Metric state space reinforcement learning for a vision-capable mobile robot

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Date

2006

Authors

Zhumatiy, Viktor
Gomez, Faustino
Hutter, Marcus
Schmidhuber, Jürgen

Journal Title

Journal ISSN

Volume Title

Publisher

IOS Press

Abstract

We address the problem of autonomously learning controllers for vision-capable mobile robots. We extend McCallum's (1995) Nearest-Sequence Memory algorithm to allow for general metrics over state-action trajectories. We demonstrate the feasibility of our

Description

Keywords

Keywords: Computer models; Discretizations; Learning controllers; Partial observability; Piecewise-continuous; Sensor inputs; State space; Algorithms; Reinforcement learning; Mobile robots Mobile robots; Reinforcement learning

Citation

URI

http://hdl.handle.net/1885/34046

Collections

ANU Research Publications

Source

Type

Book chapter

Book Title

Intelligent Autonomous Systems 9: proceedings of the 9th international conference on intelligent autonomous systems, Tokyo 2006

Entity type

Access Statement

License Rights

DOI

Restricted until

2037-12-31

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01_Zhumatiy_Metric_state_space_2006.pdf (646.07 KB)

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