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arXiv · 1206.3231

CORL: A Continuous-state Offset-dynamics Reinforcement Learner

Emma Brunskill·Bethany Leffler·Lihong Li·Michael L. Littman·Nicholas Roy
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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.

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Emma Brunskill, Bethany Leffler, Lihong Li, Michael L. Littman, Nicholas Roy. 2012-06-13. CORL: A Continuous-state Offset-dynamics Reinforcement Learner. https://arxiv.org/abs/1206.3231

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