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Reinforcement Learning for Continuous Stochastic Control Problems
R. Munos and P. Bourgine
Neural Information Processing Systems, 1997.

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Abstract

This paper is concerned with the problem of Reinforcement Learning (RL) for continuous state space and time stochastic control problems. We state the Hamilton-Jacobi-Bellman equation satisfied by the value function and use a Finite-Difference method for designing a convergent approximation scheme. Then we propose a RL algorithm based on this scheme and prove its convergence to the optimal solution.

Notes

Associated lab/group: Auton Lab
Associated project: Auton Project

Number of pages: 7

Text Reference

R. Munos and P. Bourgine, "Reinforcement Learning for Continuous Stochastic Control Problems," Neural Information Processing Systems, 1997.

BibTeX Reference

@inproceedings{Munos_1997_2942,
   author = "Remi Munos and Paul Bourgine",
   title = "Reinforcement Learning for Continuous Stochastic Control Problems",
   booktitle = "Neural Information Processing Systems",
   year = "1997"
}