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Model-based Value Iteration Algorithm for Stochastic Cleaning Robot

version 1.0.0.0 (4.91 KB) by Reza Ahmadzadeh
An Example for Reinforcement Learning and Dynamic Programming (Stochastic)

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Updated 27 Feb 2014

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Model-based value iteration Algorithm for Stochastic Cleaning Robot. This code is a very simple implementation of a value iteration algorithm, which makes it a useful start point for beginners in the field of Reinforcement learning and dynamic programming.
The stochastic 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 (0) and the final (5) states are the terminal states. The goal is to find an optimal policy that maximizes the return from any initial state. Here the Q-iteration (model-based value iteration DP). Reference: Algorithm 2-2, 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 R2011a
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