2026 Andrew G. Barto: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	83	873	842	475	456	255	98613

Research.com Recognitions

2017 - IJCAI Award for Research Excellence for his pioneering work in the theory of reinforcement learning.
2006 - IEEE Fellow For contributions to reinforcement learning methods and their neural network implementations.
2004 - Neural Networks Pioneer Award, IEEE Computational Intelligence Society
1989 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Andrew G. Barto is affiliated with the University of Massachusetts Amherst in the United States. The primary focus of their research lies within the field of Computer Science, with significant contributions to Artificial Intelligence. Their work also intersects with related subfields such as Cognitive Neuroscience, Computational Theory and Mathematics, Management Science and Operations Research, and Computational Mechanics.

The main topics covered in their research include Reinforcement Learning in Robotics, Evolutionary Algorithms and Applications, Advanced Multi-Objective Optimization Algorithms, Neural Networks and Applications, Neural dynamics and brain function, Advanced Bandit Algorithms Research, and Data Stream Mining Techniques.

They have published extensively across multiple venues, frequently contributing to:

Proceedings of the AAAI Conference on Artificial Intelligence
Scholarworks (University of Massachusetts Amherst)
IEEE Transactions on Systems Man and Cybernetics Systems
Frontiers in Neurorobotics
Communications of the ACM

Among their recent papers are:

"Learning Parameterized Skills," 2021, published in Scholarworks (University of Massachusetts Amherst)
"Looking Back on the Actor-Critic Architecture," 2020, published in IEEE Transactions on Systems Man and Cybernetics Systems
"Adaptive Step-Size for Online Temporal Difference Learning," 2021, published in Proceedings of the AAAI Conference on Artificial Intelligence
"Editorial: Intrinsically Motivated Open-Ended Learning in Autonomous Robots," 2020, published in Frontiers in Neurorobotics
"TD-DeltaPi: A Model-Free Algorithm for Efficient Exploration," 2021, published in Proceedings of the AAAI Conference on Artificial Intelligence

The scientist has collaborated with several researchers, including Bruno D. Silva, George Konidaris, Richard S. Sutton, Charles W. Anderson, and William Dabney.

Throughout their career, they have received several awards and honors:

IJCAI Award for Research Excellence in 2017 for pioneering work in the theory of reinforcement learning
IEEE Fellow in 2006 for contributions to reinforcement learning methods and their neural network implementations
Neural Networks Pioneer Award from the IEEE Computational Intelligence Society in 2004
Fellow of the American Association for the Advancement of Science (AAAS) since 1989

Best Publications

Reinforcement Learning: An Introduction

R.S. Sutton;A.G. Barto

50110
Citations
Introduction to Reinforcement Learning

Richard S. Sutton;Andrew G. Barto

6763
Citations
Neuronlike adaptive elements that can solve difficult learning control problems

Andrew G. Barto;Richard S. Sutton;Charles W. Anderson

4910
Citations
Reinforcement learning

Andrew G. Barto

4440
Citations
Toward a modern theory of adaptive networks: Expectation and prediction.

Richard S. Sutton;Andrew G. Barto

1851
Citations
Learning to act using real-time dynamic programming

Andrew G. Barto;Steven J. Bradtke;Satinder P. Singh

1650
Citations
Recent Advances in Hierarchical Reinforcement Learning

Andrew G. Barto;Sridhar Mahadevan

1580
Citations
Handbook of Learning and Approximate Dynamic Programming

Jennie Si;Andrew G. Barto;Warren Buckler Powell;Donald C. Wunsch

1029
Citations
Linear least-squares algorithms for temporal difference learning

Steven J. Bradtke;Andrew G. Barto

906
Citations
Intrinsically Motivated Reinforcement Learning

Nuttapong Chentanez;Andrew G. Barto;Satinder P. Singh

897
Citations
Improving Elevator Performance Using Reinforcement Learning

Robert H. Crites;Andrew G. Barto

894
Citations
Encyclopedia of Machine Learning and Data Mining

Unknown

893
Citations
Reinforcement learning is direct adaptive optimal control

R.S. Sutton;A.G. Barto;R.J. Williams

852
Citations
Time-Derivative Models of Pavlovian Reinforcement

Richard S. Sutton;Andrew G. Barto

798
Citations
Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks

Robert A. Jacobs;Michael I. Jordan;Andrew G. Barto

793
Citations
Learning and Sequential Decision Making

A. G. Barto;R. S. Sutton;C. J.C.H. Watkins

637
Citations
Adaptive Critics and the Basal Ganglia

Andrew G. Barto

626
Citations
Associative search network: A reinforcement learning associative memory

Andrew G. Barto;Richard S. Sutton;Peter S. Brouwer

592
Citations
Recent Advances in Hierarchical Reinforcement Learning

Unknown

581
Citations
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density

Amy McGovern;Andrew G. Barto

576
Citations
Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks

C.M. Vigorito;D. Ganesan;A.G. Barto

561
Citations
Intrinsically Motivated Learning of Hierarchical Collections of Skills

Andrew G. Barto;Satinder Singh;Nuttapong Chentanez

536
Citations

Frequent Co-Authors

George Konidaris Brown University

James C. Houk Northwestern University

Satinder Singh DeepMind (United Kingdom)

Roderic A. Grupen University of Massachusetts Amherst

Warren B. Powell Princeton University

Balaraman Ravindran Indian Institute of Technology Madras

Charles W. Anderson Colorado State University

Robert A. Jacobs University of Rochester

Sridhar Mahadevan University of Massachusetts Amherst

Matthew Botvinick Yale University

External Links

Wikipedia of Andrew G. Barto Personal website of Andrew G. Barto Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Expanding your expertise beyond computer science can open a variety of career pathways. For those considering leadership or management roles alongside their tech background, executive mba programs blend business strategy with advanced technical skills. This path can be ideal if you're aiming for senior management positions within tech companies.

If information science or digital archiving interests you, pursuing an online MLIS can be a strategic choice. There are several mlis programs available online that focus on library and information science, often integrating tech skills with information management.

Many students seek flexible and cost-effective ways to earn advanced degrees. Numerous affordable master's degrees online cover a wide array of fields, including technology, business, leadership, and more—enabling specialization without breaking the bank.

If you aspire to reach the top ranks of academia or corporate leadership, consider a online phd in leadership. These programs help tech professionals transition into educational management, C-suite roles, or consulting positions by developing strategic and organizational skills.