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Abstract

In order to find the optimal control of continuous state-space and time reinforcement learning (RL) problems, we approximate the value function (VF) with a particular class of functions called the barycentric interpolators. We establish sufficient conditions under which a RL algorithm converges to the optimal VF, even when we use approximate models of the state dynamics and the reinforcement functions.

Keywords

Reinforcement learning, optimal control, discretization methods

Notes

Associated Lab(s) / Group(s): Auton Lab

Associated Project(s): Auton Project

Number of pages: 7

Text Reference

Remi Munos and Andrew Moore, "Barycentric Interpolator for Continuous Space and Time Reinforcement Learning," Neural Information Processing Systems, December, 1998.

BibTeX Reference

@inproceedings{Munos_1998_2094,
   author = "Remi Munos and Andrew Moore",
   title = "Barycentric Interpolator for Continuous Space and Time Reinforcement Learning",
   booktitle = "Neural Information Processing Systems",
   publisher = "MIT Press",
   month = "December",
   year = "1998",
   volume = "11",
}

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