NDLI: Implementation of Q — Learning algorithm for solving maze problem

Content Provider	IEEE Xplore Digital Library
Author	Osmankovic, D. Konjicija, S.
Copyright Year	2011
Description	Author affiliation: Department of Automatic Control and Electronics, University in Sarajevo, Faculty of Electrical Engineering in Sarajevo, Zmaja od Bosne bb, 71000 Sarajevo, Bosnia and Herzegovina (Osmankovic, D.; Konjicija, S.)
Abstract	Machine learning is very important in several fields ranging from control systems to data mining. This paper presents Q — Learning implementation for abstract graph models with maze solving (finding the trajectory out of the maze) taken as example of graph problem. The paper consists of conversion of maze matrix to Q — Learning reward matrix, and also the implementation of Q — Learning algorithm for the reward matrix (similar to minimizing criteria matrix in dynamic programming). This implementation is on higher level of abstraction, so other representations can be used (artificial neural networks, tree etc.). For the testing of Q — Learning algorithm, maze solving problem was visualized in MATLAB programming language with the found trajectory marked on the maze. The maze in this paper is defined with starting position in the top left corner and the exit in the bottom right corner. The performance of the algorithm is measured for different scales of the problem.
Starting Page	1619
Ending Page	1622
File Size	907345
Page Count	4
File Format	PDF
ISBN	9781457709968
e-ISBN	9789532330595
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	Croatia
Access Restriction	Subscribed
Rights Holder	MIPRO
Subject Keyword	Agent Reinforcement learning Maze Artificial intelligence Machine intelligence
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Q-leaming vs. FRIQ-leaming in the Maze problem

An Efficient Hardware Implementation of Reinforcement Learning: The Q-Learning Algorithm

Reinforcement learning accelerated with artificial neural network for maze and search problems

Multi-Step Truncated Q Learning Algorithm

Reinforcement learning generalization using state aggregation with a maze-solving problem

Investigation of independent reinforcement learning algorithms in multi-agent environments

The implementation of reinforcement learning algorithms on the elevator control system

Applying Reinforcement Learning to the Weapon Assignment Problem in Air Defence

A reinforcement learning intelligent agent.

Implementation of Q — Learning algorithm for solving maze problem

Similar Documents

Q-leaming vs. FRIQ-leaming in the Maze problem

An Efficient Hardware Implementation of Reinforcement Learning: The Q-Learning Algorithm

Reinforcement learning accelerated with artificial neural network for maze and search problems

Multi-Step Truncated Q Learning Algorithm

Reinforcement learning generalization using state aggregation with a maze-solving problem

Investigation of independent reinforcement learning algorithms in multi-agent environments

The implementation of reinforcement learning algorithms on the elevator control system

Applying Reinforcement Learning to the Weapon Assignment Problem in Air Defence

A reinforcement learning intelligent agent.

Implementation of Q — Learning algorithm for solving maze problem