NDLI: Fast reinforcement learning for simple physical robots

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Fast reinforcement learning for simple physical robots

Content Provider	Springer Nature Link
Author	Hartono, Pitoyo Kakita, Sachiko
Copyright Year	2009
Abstract	In the past few years, the field of autonomous robot has been rigorously studied and non-industrial applications of robotics are rapidly emerging. One of the most interesting aspects of this field is the development of the learning ability which enables robots to autonomously adapt to given environments without human guidance. As opposed to the conventional methods of robots’ control, where human logically design the behavior of a robot, the ability to acquire action strategies through some learning processes will not only significantly reduce the production costs of robots but also improves the applicability of robots in wider tasks and environments. However, learning algorithms usually require large calculation cost, which make them unsuitable for robots with limited resources. In this study, we propose a simple two-layered neural network that implements a novel and fast Reinforcement Learning. The proposed learning method requires significantly less calculation resources, hence is applicable to small physical robots running in the real world environments. For this study, we built several simple robots and implemented the proposed learning mechanism to them. In the experiments, to evaluate the efficacy of the proposed learning mechanism, several robots were simultaneously trained to acquire obstacle avoidance strategies in the same environment, thus, forming a dynamic environment where the learning task is substantially harder than in the case of learning in a static environment and promising result was obtained.
Starting Page	305
Ending Page	313
Page Count	9
File Format	PDF
ISSN	18659284
Journal	Memetic Computing
Volume Number	1
Issue Number	4
e-ISSN	18659292
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-09-25
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Autonomous robot Reinforcement learning Neural network Competitive learning Applications of Mathematics Bioinformatics Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) ApplicationMathematics/Computational Methods of Engineering Control Robotics Mechatronics
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Computer Science

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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
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