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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(s) / Group(s): Auton Lab

Associated Project(s): Auton Project

Number of pages: 7

Text Reference

Remi Munos and Paul 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",
}

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