5th International Conference on Intelligent Computing, ICIC 2009 , 2009-09-16

Title : ( Using control theory for analysis of reinforcement learning and optimal policy properties in grid-world problems )

Authors: Seyyed Mostapha Kalami , Mohammad Bagher Naghibi Sistani , Naser Pariz ,

Citation: BibTeX | EndNote

Abstract

Markov Decision Process (MDP) has enormous applications in science, engineering, economics and management. Most of decision processes have Markov property and can be modeled as MDP. Reinforcement Learning (RL) is an approach to deal with MDPs. RL methods are based on Dynamic Programming (DP) algorithms, such as Policy Evaluation, Policy Iteration and Value Iteration. In this paper, policy evaluation algorithm is represented in the form of a discrete-time dynamical system. Hence, using Discrete-Time Control methods, behavior of agent and properties of various policies, can be analyzed. The general case of grid-world problems is addressed, and some important results are obtained for this type of problems as a theorem. For example, equivalent system of an optimal policy for a grid-world problem is dead-beat.

Keywords

, Discrete, Time Control Systems; Dynamic Programming; Markov Decision Process; Reinforcement Learning; Stochastic Control
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@inproceedings{paperid:1020300,
author = {Kalami, Seyyed Mostapha and Naghibi Sistani, Mohammad Bagher and Pariz, Naser},
title = {Using control theory for analysis of reinforcement learning and optimal policy properties in grid-world problems},
booktitle = {5th International Conference on Intelligent Computing, ICIC 2009},
year = {2009},
location = {Ulsan, south korea},
keywords = {Discrete-Time Control Systems; Dynamic Programming; Markov Decision Process; Reinforcement Learning; Stochastic Control},
}

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%0 Conference Proceedings
%T Using control theory for analysis of reinforcement learning and optimal policy properties in grid-world problems
%A Kalami, Seyyed Mostapha
%A Naghibi Sistani, Mohammad Bagher
%A Pariz, Naser
%J 5th International Conference on Intelligent Computing, ICIC 2009
%D 2009

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