Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot
<|reference_start|>Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot: 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 approach by successfully running our algorithm on a real mobile robot. The algorithm is novel and unique in that it (a) explores the environment and learns directly on a mobile robot without using a hand-made computer model as an intermediate step, (b) does not require manual discretization of the sensor input space, (c) works in piecewise continuous perceptual spaces, and (d) copes with partial observability. Together this allows learning from much less experience compared to previous methods.<|reference_end|>
@article{zhumatiy2006metric,
    title={Metric State Space Reinforcement Learning for a Vision-Capable Mobile
  Robot},
    author={Viktor Zhumatiy and Faustino Gomez and Marcus Hutter and Juergen
  Schmidhuber},
    journal={Proc. 9th International Conf. on Intelligent Autonomous Systems
  (IAS 2006) pages 272-281},
    year={2006},
    number={IDSIA-05-06},
    archivePrefix={arXiv},
    eprint={cs/0603023},
    primaryClass={cs.RO cs.LG}
}