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Model-based Policy Iteration Algorithm for Deterministic Cleaning Robot

version 1.0.0.0 (5.29 KB) by Reza Ahmadzadeh
An Example for Reinforcement Learning using Model-based Policy iteration Approach

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Updated 17 Mar 2014

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Model-based policy iteration Algorithm for Deterministic Cleaning Robot. This code is a very simple implementation of a policy iteration algorithm, which makes it a useful start point for beginners in the field of Reinforcement learning and dynamic programming.
The deterministic cleaning-robot MDP: a cleaning robot has to collect a used can also has to recharge its batteries. the state describes the position of the robot and the action describes the direction of motion. The robot can move to the left or to the right. The first (1) and the final (6) states are the terminal states. The goal is to find an optimal policy that maximizes the return from any initial state. Here the policy-iteration (model-based policy iteration DP). Reference: Algorithm 2-5, from:
@book{busoniu2010reinforcement,
title={Reinforcement learning and dynamic programming using function approximators},
author={Busoniu, Lucian and Babuska, Robert and De Schutter, Bart and Ernst, Damien},
year={2010},
publisher={CRC Press}
}

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MATLAB Release Compatibility
Created with R2010a
Compatible with any release
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