Search Results - "Barto, Andrew G."

Rediscovering Reinforcement Learning.

Authors: Barto, Andrew G.¹ (AUTHOR) barto@cs.umass.edu

Source: Communications of the ACM. Dec2025, Vol. 68 Issue 12, p98-102. 5p.

Subject Terms: *TECHNOLOGICAL progress, *GOVERNMENT aid to research, REINFORCEMENT learning

Company/Entity: UNITED States. Air Force. Office of Scientific Research

Preventing undesirable behavior of intelligent machines

Authors: Thomas, Philip S. da Silva, Bruno Castro Barto, Andrew G. et al.

Source: Science, 2019 Nov . 366(6468), 999-1004.

Access URL: https://www.jstor.org/stable/26861297

Looking Back on the Actor–Critic Architecture

Authors: Barto, Andrew G. Sutton, Richard S. Anderson, Charles W.

Source: IEEE Transactions on Systems, Man, and Cybernetics: Systems ; volume 51, issue 1, page 40-50 ; ISSN 2168-2216 2168-2232

Availability: https://doi.org/10.1109/tsmc.2020.3041775
http://xplorestaging.ieee.org/ielx7/6221021/9321226/09306925.pdf?arnumber=9306925

Enriching behavioral ecology with reinforcement learning methods

Authors: Frankenhuis, Willem E. Panchanathan, Karthik Barto, Andrew G. et al.

Contributors: Frankenhuis, Willem E. Panchanathan, Karthik Barto, Andrew G. et al.

Source: Behavioural Processes ; volume 161, page 94-100 ; ISSN 0376-6357

Availability: http://dx.doi.org/10.1016/j.beproc.2018.01.008
https://api.elsevier.com/content/article/PII:S0376635717303637?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S0376635717303637?httpAccept=text/plain

Adaptive Real-Time Dynamic Programming

Authors: Barto, Andrew G.

Source: Encyclopedia of Machine Learning and Data Science ; page 1-4 ; ISBN 9781489975027 9781489975027

Availability: http://dx.doi.org/10.1007/978-1-4899-7502-7_5-1
https://link.springer.com/content/pdf/10.1007/978-1-4899-7502-7_5-1

Approximate Optimal Control as a Model for Motor Learning

Authors: Berthier, Neil E. Rosenstein, Michael T. Barto, Andrew G.

Source: Psychological Review. Apr 2005 112(2):329-346.

Peer Reviewed: Y

Page Count: 18

Descriptors: Psychology, Cognitive Development, Models, Simulation, Child Development, Psychomotor Skills

Access URL: https://www.apa.org/journals

Reinforcement learning: an introduction

Authors: Sutton, Richard S. Barto, Andrew G.

Subject Terms: Aprendizaje por refuerzo, Inteligencia adaptativa artificial, Sistemas adaptativos, Aprendizaje por refuerzo (Inteligencia artificial), Inteligencia artificial - Historia, Redes neuronales artificiales (Informática), Reinforcement Learning (Artificial Intelligence), Artificial Intelligence - History, Artificial Neural Networks (Computer Science)

File Description: 352 páginas; text/html

Relation: https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf; https://hdl.handle.net/20.500.12010/36952

Availability: https://hdl.handle.net/20.500.12010/36952
https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf

Linear Least-Squares Algorithms for Temporal Difference Learning: Linear least-squares algorithms for temporal difference learning

Authors: Bradtke, Steven J. Barto, Andrew G.

Source: Recent Advances in Reinforcement Learning ISBN: 9780792397052

Subject Terms: Markov Decision Problems, Computer Sciences, 4. Education, Least-Squares, Learning and adaptive systems in artificial intelligence, 0211 other engineering and technologies, least-squares TD, 02 engineering and technology, TD error variance, Temporal Difference Methods, Reinforcement learning, 0202 electrical engineering, electronic engineering, information engineering, Parallel algorithms in computer science, temporal difference algorithms

File Description: application/xml; application/pdf

Access URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1016&context=cs_faculty_pubs
https://link.springer.com/content/pdf/10.1023/A:1018056104778.pdf
https://link.springer.com/content/pdf/10.1007%2FBF00114723.pdf
https://zbmath.org/868032
https://doi.org/10.1007/bf00114723
https://dblp.uni-trier.de/db/journals/ml/ml22.html#BradtkeB96
https://works.bepress.com/andrew_barto/2/download/
https://link.springer.com/content/pdf/10.1023%2FA%3A1018056104778.pdf
https://www.mendeley.com/catalogue/b87444f8-bd93-3732-9251-6cb792daee48/
https://works.bepress.com/andrew_barto/2/
https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1016&context=cs_faculty_pubs

An intrinsic reward mechanism for efficient exploration

Authors: Şimşek, Özgür Barto, Andrew G.

Source: Proceedings of the 23rd international conference on Machine learning - ICML '06. :833-840

Subject Terms: 0209 industrial biotechnology, Computer Sciences, 9. Industry and infrastructure, 4. Education, 0202 electrical engineering, electronic engineering, information engineering, 14. Life underwater, 02 engineering and technology, 16. Peace & justice, 7. Clean energy

File Description: application/pdf

Access URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1021&context=cs_faculty_pubs
https://scholarworks.umass.edu/cs_faculty_pubs/4/
http://portal.acm.org/citation.cfm?doid=1143844.1143949
https://dblp.uni-trier.de/db/conf/icml/icml2006.html#SimsekB06
https://dl.acm.org/citation.cfm?id=1143949
http://www-all.cs.umass.edu/pubs/2006/simsek_b_icml06.pdf
https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1021&context=cs_faculty_pubs

Identifying useful subgoals in reinforcement learning by local graph partitioning

Authors: Şimşek, Özgür Wolfe, Alicia P. Barto, Andrew G.

Source: Proceedings of the 22nd international conference on Machine learning - ICML '05. :816-823

Subject Terms: 0209 industrial biotechnology, Computer Sciences, 13. Climate action, 4. Education, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 14. Life underwater, 15. Life on land, 16. Peace & justice, 7. Clean energy

File Description: application/pdf

Access URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1006&context=cs_faculty_pubs
https://researchportal.bath.ac.uk/en/publications/identifying-useful-subgoals-in-reinforcement-learning-by-local-gr
http://portal.acm.org/citation.cfm?doid=1102351.1102454
http://scholarworks.umass.edu/cs_faculty_pubs/19/
https://dblp.uni-trier.de/db/conf/icml/icml2005.html#SimsekWB05
http://dblp.uni-trier.de/db/conf/icml/icml2005.html#SimsekWB05
https://doi.org/10.1145/1102351.1102454

Learning and generalization of complex tasks from unstructured demonstrations

Authors: Niekum, Scott Osentoski, Sarah Konidaris, George et al.

Source: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems ; page 5239-5246

Availability: https://doi.org/10.1109/iros.2012.6386006
http://xplorestaging.ieee.org/ielx5/6363628/6385431/06386006.pdf?arnumber=6386006

On Ensuring that Intelligent Machines Are Well-Behaved

Authors: Thomas, Philip S. da Silva, Bruno Castro Barto, Andrew G. et al.

Subject Terms: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: http://arxiv.org/abs/1708.05448

Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks

Authors: Vigorito, Christopher M. Ganesan, Deepak Barto, Andrew G.

Source: 2007 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks

Availability: https://doi.org/10.1109/sahcn.2007.4292814
http://xplorestaging.ieee.org/ielx5/4292804/4292805/04292814.pdf?arnumber=4292814

Using relative novelty to identify useful temporal abstractions in reinforcement learning

Authors: Şimşek, Özgür Barto, Andrew G.

Source: Twenty-first international conference on Machine learning - ICML '04 ; page 95

Availability: https://doi.org/10.1145/1015330.1015353
https://dl.acm.org/doi/10.1145/1015330.1015353
http://dl.acm.org/ft_gateway.cfm?id=1015353%26amp%3Bftid=273135%26amp%3Bdwn=1

Commentary on Utility and Bounds

Authors: Barto, Andrew G.

Source: Topics in Cognitive Science ; volume 6, issue 2, page 338-341 ; ISSN 1756-8757 1756-8765

Availability: http://dx.doi.org/10.1111/tops.12090
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Ftops.12090
https://onlinelibrary.wiley.com/doi/pdf/10.1111/tops.12090

Learning grounded finite-state representations from unstructured demonstrations

Authors: Niekum, Scott Osentoski, Sarah Konidaris, George et al.

Source: The International Journal of Robotics Research ; volume 34, issue 2, page 131-157 ; ISSN 0278-3649 1741-3176

Availability: https://doi.org/10.1177/0278364914554471
https://journals.sagepub.com/doi/pdf/10.1177/0278364914554471
https://journals.sagepub.com/doi/full-xml/10.1177/0278364914554471

Optimal Behavioral Hierarchy

Authors: Solway, Alec Diuk, Carlos Córdova, Natalia et al.

Contributors: Solway, Alec Diuk, Carlos Córdova, Natalia et al.

Source: PLoS Computational Biology ; volume 10, issue 8, page e1003779 ; ISSN 1553-7358

Availability: https://doi.org/10.1371/journal.pcbi.1003779
http://dx.plos.org/10.1371/journal.pcbi.1003779

A Computational Hypothesis for Allostasis: Delineation of Substance Dependence, Conventional Therapies, and Alternative Treatments

Authors: Levy, Yariv Z. Levy, Dino J. Barto, Andrew G. et al.

Source: Frontiers in Psychiatry ; volume 4 ; ISSN 1664-0640

Availability: http://dx.doi.org/10.3389/fpsyt.2013.00167

A Neural Signature of Hierarchical Reinforcement Learning

Authors: Ribas-Fernandes, José J.F. Solway, Alec Diuk, Carlos et al.

Source: Neuron ; volume 71, issue 2, page 370-379 ; ISSN 0896-6273

Availability: http://dx.doi.org/10.1016/j.neuron.2011.05.042
https://api.elsevier.com/content/article/PII:S0896627311004995?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S0896627311004995?httpAccept=text/plain

Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective

Authors: Singh, Satinder Lewis, Richard L. Barto, Andrew G. et al.

Source: IEEE Transactions on Autonomous Mental Development ; volume 2, issue 2, page 70-82 ; ISSN 1943-0604 1943-0612

Availability: https://doi.org/10.1109/tamd.2010.2051031
http://xplorestaging.ieee.org/ielx5/4563672/5497035/05471106.pdf?arnumber=5471106