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• W2951084826 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." @default.
• W2951084826 created "2019-06-27" @default.
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• W2951084826 date "2012-06-13" @default.
• W2951084826 modified "2023-09-24" @default.
• W2951084826 title "CORL: A Continuous-state Offset-dynamics Reinforcement Learner" @default.
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