NDLI: Learning to reach by reinforcement learning using a receptive field based function approximation approach with continuous actions

Content Provider	Springer Nature Link
Author	Tamosiunaite, Minija Asfour, Tamim Wörgötter, Florentin
Copyright Year	2009
Abstract	Reinforcement learning methods can be used in robotics applications especially for specific target-oriented problems, for example the reward-based recalibration of goal directed actions. To this end still relatively large and continuous state-action spaces need to be efficiently handled. The goal of this paper is, thus, to develop a novel, rather simple method which uses reinforcement learning with function approximation in conjunction with different reward-strategies for solving such problems. For the testing of our method, we use a four degree-of-freedom reaching problem in 3D-space simulated by a two-joint robot arm system with two DOF each. Function approximation is based on 4D, overlapping kernels (receptive fields) and the state-action space contains about 10,000 of these. Different types of reward structures are being compared, for example, reward-on- touching-only against reward-on-approach. Furthermore, forbidden joint configurations are punished. A continuous action space is used. In spite of a rather large number of states and the continuous action space these reward/punishment strategies allow the system to find a good solution usually within about 20 trials. The efficiency of our method demonstrated in this test scenario suggests that it might be possible to use it on a real robot for problems where mixed rewards can be defined in situations where other types of learning might be difficult.
Starting Page	249
Ending Page	260
Page Count	12
File Format	PDF
ISSN	03401200
Journal	Biological Cybernetics
Volume Number	100
Issue Number	3
e-ISSN	14320770
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-02-20
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Reinforcement learning Function approximation Robot control Neurosciences Computer Application in Life Sciences Neurobiology Bioinformatics Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Computer Science Biotechnology

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

Learning to reach by reinforcement learning using a receptive field based function approximation approach with continuous actions

Stochastic resonance in visual sensitivity

Learning strategies in table tennis using inverse reinforcement learning

Modular inverse reinforcement learning for visuomotor behavior

Learning with incomplete information in the committee machine

Predictive coding accounts for V1 response properties recorded using reverse correlation

Neurocomputational model of moral behaviour

Emotions in robot psychology

Learning robotic eye–arm–hand coordination from human demonstration: a coupled dynamical systems approach

Learning to reach by reinforcement learning using a receptive field based function approximation approach with continuous actions

Similar Documents

Learning to reach by reinforcement learning using a receptive field based function approximation approach with continuous actions

Stochastic resonance in visual sensitivity

Learning strategies in table tennis using inverse reinforcement learning

Modular inverse reinforcement learning for visuomotor behavior

Learning with incomplete information in the committee machine

Predictive coding accounts for V1 response properties recorded using reverse correlation

Neurocomputational model of moral behaviour

Emotions in robot psychology

Learning robotic eye–arm–hand coordination from human demonstration: a coupled dynamical systems approach

Learning to reach by reinforcement learning using a receptive field based function approximation approach with continuous actions