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RI | Publications | Using Finite-Differences methods for approximating the value function of continuous Reinforcement Learning problems
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Using Finite-Differences methods for approximating the value function of continuous Reinforcement Learning problems
R. Munos
International Symposium on Multi-Technology Information Processing 1996, 1996.
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| Abstract |
This paper presents a reinforcement learning method for solving continuous optimal control problems when the dynamics of the system is unknown. First, we use a Finite Differences method for discretizing the Hamilton-Jacobi-Bellman equation and obtain a finite Markovian Decision Process. This permits us to approximate the value function of the continuous problem with piecewise constant functions defined on a grid. Then we propose to solve this MDP on-line with the available knowledge using a direct and convergent reinforcement learning algorithm, called the Finite-Differences Reinforcement Learning
| Notes |
Associated lab/group: Auton Lab
Associated project: Auton Project
Number of pages: 6
| Text Reference |
R. Munos, "Using Finite-Differences methods for approximating the value function of continuous Reinforcement Learning problems," International Symposium on Multi-Technology Information Processing 1996, 1996.
| BibTeX Reference |
@inproceedings{Munos_1996_2946,
author = "Remi Munos",
title = "Using Finite-Differences methods for approximating the value function of continuous Reinforcement Learning problems",
booktitle = "International Symposium on Multi-Technology Information Processing 1996",
year = "1996"
}