Uncertainty in Artificial Intelligence
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CORL: A Continuous-state Offset-dynamics Reinforcement Learner
Emma Brunskill, Bethany Leffler, Lihong Li, Michael Littman, Nicholas Roy
Abstract:
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinforcementlearning algorithm for learning in these domains and prove for certain environments the algorithm is probably approximately correct with a sample complexity that scales polynomially with the state-space dimension. Unfortunately, no optimal planning techniques exist in general for such problems; instead we use fitted value iteration to solve the learned MDP, and include the error due to approximate planning in our bounds. Finally, we report an experiment using a robotic car driving over varying terrain to demonstrate that these dynamics representations adequately capture real-world dynamics and that our algorithm can be used to efficiently solve such problems.
Keywords:
Pages: 53-61
PS Link:
PDF Link: /papers/08/p53-brunskill.pdf
BibTex:
@INPROCEEDINGS{Brunskill08,
AUTHOR = "Emma Brunskill and Bethany Leffler and Lihong Li and Michael Littman and Nicholas Roy",
TITLE = "CORL: A Continuous-state Offset-dynamics Reinforcement Learner",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "53--61"
}


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