Q-learning (Model-free Value Iteration) Algorithm for Deterministic Cleaning Robot

An Example for Reinforcement Learning using Q-learning with epsilon-greedy exploration
Updated 5 Mar 2014

View License

Q-learning with epsilon-greedy exploration Algorithm for Deterministic Cleaning Robot V1
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 Q-learning epsilon-greedy exploration
algorithm (in Reinforcement learning) is used.
Algorithm 2-3, from:
title={Reinforcement learning and dynamic programming using function approximators},
author={Busoniu, Lucian and Babuska, Robert and De Schutter, Bart and Ernst, Damien},
publisher={CRC Press}

Cite As

Reza Ahmadzadeh (2024). Q-learning (Model-free Value Iteration) Algorithm for Deterministic Cleaning Robot (https://www.mathworks.com/matlabcentral/fileexchange/45759-q-learning-model-free-value-iteration-algorithm-for-deterministic-cleaning-robot), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2012a
Compatible with any release
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Version Published Release Notes