NDLI: A topological reinforcement learning agent for navigation

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Neural Computing and Applications : Volume 12, Issue 3-4, December 2003

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A topological reinforcement learning agent for navigation

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A topological reinforcement learning agent for navigation

Content Provider	Springer Nature Link
Author	Braga, Arthur P. S. Araújo, Aluízio F. R.
Copyright Year	2003
Abstract	This article proposes a reinforcement learning procedure for mobile robot navigation using a latent-like learning schema. Latent learning refers to learning that occurs in the absence of reinforcement signals and is not apparent until reinforcement is introduced. This concept considers that part of a task can be learned before the agent receives any indication of how to perform such a task. In the proposed topological reinforcement learning agent (TRLA), a topological map is used to perform the latent learning. The propagation of the reinforcement signal throughout the topological neighborhoods of the map permits the estimation of a value function which takes in average less trials and with less updatings per trial than six of the main temporal difference reinforcement learning algorithms: Q-learning, SARSA, Q(λ)-learning, SARSA(λ), Dyna-Q and fast Q(λ)-learning. The RL agents were tested in four different environments designed to consider a growing level of complexity in accomplishing navigation tasks. The tests suggested that the TRLA chooses shorter trajectories (in the number of steps) and/or requires less value function updatings in each trial than the other six reinforcement learning (RL) algorithms.
Starting Page	220
Ending Page	236
Page Count	17
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	12
Issue Number	3-4
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2003-11-19
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Latent learning Navigation Neural networks Reinforcement learning Topological maps
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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

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Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network

Neural Network Architecture for Cognitive Navigation in Dynamic Environments

Hierarchical multi-agent reinforcement learning

A Reinforcement Learning Scheme for a Partially-Observable Multi-Agent Game

Erratum to: Reinforcement learning and neural networks for multi-agent nonzero-sum games of nonlinear constrained-input systems

A topological reinforcement learning agent for navigation

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