A. Barto

James C. Houk, Nathan Sitkoff, Andrew H. Fagg, 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

Models of the cerebellum and motor learning

A. Barto, J. Houk, J. Buckingham, 1996 .

Behavioral building blocks for autonomous agents: description, identification, and learning

Andrew G. Barto, Özgür Simsek, A. Barto, 2008 .

Local Bandit Approximation for Optimal Learning Problems

Andrew G. Barto, Michael O. Duff, A. Barto, 1996, NIPS.

The IM-CLeVeR Project : Intrinsically Motivated Cumulative Learning Versatile Robots

Julian Togelius, Francesco Mannella, Jürgen Schmidhuber, 2009 .

Enriching behavioral ecology with reinforcement learning methods

Andrew G. Barto, Willem E. Frankenhuis, Karthik Panchanathan, 2018, Behavioural Processes.

SMDP Homomorphisms: An Algebraic Approach to Abstraction in Semi-Markov Decision Processes

Balaraman Ravindran, Andrew G. Barto, A. Barto, 2003, IJCAI.

Movements Under Resistive and Assistive Force Fields Encoding of Movement Dynamics by Purkinje Cell Simple Spike Activity During Fast Arm

Thomas Sinkjær, Mitsuo Kawato, Ashvin Shah, 2008 .

Learning Instance-Independent Value Functions to Enhance Local Search

Richard S. Sutton, Andrew G. Barto, Theodore J. Perkins, 1998, NIPS.

Cerebellar control of endpoint position-a simulation model

James C. Houk, Andrew G. Barto, C. H. Wu, 1990, 1990 IJCNN International Joint Conference on Neural Networks.

A causal approach to hierarchical decomposition of factored MDPs

Andrew G. Barto, Anders Jonsson, A. Barto, 2005, ICML.

Hierarchical Decision Making

Warren B. Powell, Jennie Si, Andrew G. Barto, 2004 .

The emergence of movement units through learning with noisy efferent signals and delayed sensory feedback

Andrew G. Barto, Michael Kositsky, A. Barto, 2002, Neurocomputing.

Competence progress intrinsic motivation

Andrew G. Barto, Andrew Stout, A. Barto, 2010, 2010 IEEE 9th International Conference on Development and Learning.

Reinforcement Learning for Mixed Open-loop and Closed-loop Control

Shlomo Zilberstein, Eric A. Hansen, Andrew G. Barto, 1996, NIPS.

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

Andrew G. Barto, Deepak Ganesan, Christopher M. Vigorito, 2007, 2007 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

Variable risk control via stochastic optimization

Scott Kuindersma, Roderic A. Grupen, Andrew G. Barto, 2013, Int. J. Robotics Res..

Transfer in Reinforcement Learning via Shared Features

Andrew G. Barto, George Konidaris, Ilya Scheidwasser, 2012, J. Mach. Learn. Res..

Automated State Abstraction for Options using the U-Tree Algorithm

Andrew G. Barto, Anders Jonsson, A. Barto, 2000, NIPS.

Simulation of networks using multidimensional Fast Fourier Transforms

Andrew G. Barto, A. Barto, 1974, SIML.

Cerebellar learning for control of a two-link arm in muscle space

James C. Houk, Nathan Sitkoff, Andrew H. Fagg, 1997, Proceedings of International Conference on Robotics and Automation.

Active Learning of Parameterized Skills

Bruno Castro da Silva, Andrew G. Barto, George Konidaris, 2014, ICML.

Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories

Scott Kuindersma, Roderic A. Grupen, Andrew G. Barto, 2010, NIPS.

Temporal difference learning

Andrew G. Barto, A. Barto, 2007, Scholarpedia.

Behavioral Hierarchy: Exploration and Representation

Andrew G. Barto, George Konidaris, Christopher M. Vigorito, 2013, Computational and Robotic Models of the Hierarchical Organization of Behavior.

Improving Intelligent Tutoring Systems: Using Expectation Maximization to Learn Student Skill Levels

Beverly Park Woolf, Sridhar Mahadevan, Andrew G. Barto, 2006, Intelligent Tutoring Systems.

Hierarchical Representations of Behavior for Efficient Creative Search

Andrew G. Barto, Christopher M. Vigorito, A. Barto, 2008, AAAI Spring Symposium: Creative Intelligent Systems.

CHAMP: Changepoint Detection Using Approximate Model Parameters

Christopher G. Atkeson, Scott Niekum, Andrew G. Barto, 2014 .

Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts

Andrew G. Barto, J. Eliot B. Moss, Amy McGovern, 2002, Machine Learning.

Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery

Scott Niekum, Andrew G. Barto, A. Barto, 2011, Lifelong Learning.

A Neural Network Simulation MethodUsing the FastFourier Transform

Andrew G. Barto, A. Barto, 1976 .

Adaptive Step-Size for Online Temporal Difference Learning

Andrew G. Barto, William Dabney, A. Barto, 2012, AAAI.

Symmetries and Model Minimization in Markov Decision Processes

Balaraman Ravindran, Andrew G. Barto, A. Barto, 2001 .

Cooperativity in Networks of Pattern Recognizing Stochastic Learning Automata

P. Anandan, Andrew G. Barto, Charles W. Anderson, 1986 .

Decision Tree Methods for Finding Reusable MDP Homomorphisms

Alicia P. Wolfe, Andrew G. Barto, A. Barto, 2006, AAAI.

On Separating Agent Designer Goals from Agent Goals : Breaking the Preferences – Parameters Confound

A. Barto, Satinder Singh, 2010 .

Large-scale dynamic optimization using teams of reinforcement learning agents

Andrew G. Barto, Robert H. Crites, A. Barto, 1996 .

Connectionist learning for control

Andrew G. Barto, A. Barto, 1990 .

Where Do Rewards Come From

Richard L. Lewis, Andrew G. Barto, Satinder Singh, 2009 .

Approximate optimal control as a model for motor learning.

A. Barto, M. Rosenstein, N. Berthier, 2005, Psychological review.

Learning Parameterized Skills

Bruno Castro da Silva, Andrew G. Barto, George Konidaris, 2012, ICML.

Relativized Options: Choosing the Right Transformation

Balaraman Ravindran, Andrew G. Barto, A. Barto, 2003, ICML.

Reinforcement learning control

Andrew G. Barto, A. Barto, 1994, Current Opinion in Neurobiology.

Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective

Richard L. Lewis, Andrew G. Barto, Satinder P. Singh, 2010, IEEE Transactions on Autonomous Mental Development.

Conjugate Markov Decision Processes

Andrew G. Barto, Philip S. Thomas, A. Barto, 2011, ICML.

Intrinsically Motivated Hierarchical Skill Learning in Structured Environments

Andrew G. Barto, Christopher M. Vigorito, A. Barto, 2010, IEEE Transactions on Autonomous Mental Development.

Incremental Structure Learning in Factored MDPs with Continuous States and Actions

Christopher M. Vigorito, A. Barto, 2009 .

Connectionist learning for control: an overview

Andrew G. Barto, A. Barto, 1990 .

Learning admittance mappings for force-guided assembly

Roderic A. Grupen, Andrew G. Barto, Vijaykumar Gullapalli, 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

Combining Reinforcement Learning with a Local Control Algorithm

Andrew G. Barto, Michael T. Rosenstein, Jette Randløv, 2000, ICML.

PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning

Andrew G. Barto, Marc Pickett, A. Barto, 2002, ICML.

Autonomous Skill Acquisition on a Mobile Manipulator

Scott Kuindersma, Roderic A. Grupen, Andrew G. Barto, 2011, AAAI.

Autonomous discovery of temporal abstractions from interaction with an environment

Andrew G. Barto, Elizabeth Amy Mcgovern, A. Barto, 2002 .

Statistical Machine Learning for Large-Scale Optimization

Kee-Eung Kim, Thomas G. Dietterich, Russell Impagliazzo, 2000 .

Neural Networks and Adaptive Control

Andrew G. Barto, Vijaykumar Gullapalli, A. Barto, 1993 .

Prediction of complex two-dimensional trajectories by a cerebellar model of smooth pursuit eye movement.

A G Barto, R E Kettner, S Mahamud, 1997, Journal of neurophysiology.

ADP: Goals, Opportunities and Principles

Jennie Si, Don Wunsch, Andrew G. Barto, 2004 .

Intrinsically Motivated Reinforcement Learning

Nuttapong Chentanez, Andrew G. Barto, Satinder P. Singh, 2004, NIPS.

Intrinsic motivations and open-ended development in animals, humans, and robots: an overview

A. Barto, R. Ryan, P. Redgrave, 2014, Front. Psychol..

Lyapunov Design for Safe Reinforcement Learning Control

A. Barto, T. Perkins, 2002 .

Adaptive Critic Based Neural Network for ControlConstrained Agile Missile

Jennie Si, Don Wunsch, Andrew G. Barto, 2004 .

GUIDANCE IN THE USE OF ADAPTIVE CRITICS FOR CONTROL

Warren B. Powell, Jennie Si, Andrew G. Barto, 2007 .

Learning to reach via corrective movements

A. Barto, 1999 .

Reinforcement learning is direct adaptive optimal control

R.J. Williams, R.S. Sutton, A.G. Barto, 1991, IEEE Control Systems.

Evaluating the Feasibility of Learning Student Models from Data

Hasmik Mehranian, Donald L. Fisher, Beverly Park Woolf, 2005 .

Learning from a Single Demonstration: Motion Planning with Skill Segmentation

Scott Kuindersma, Roderic A. Grupen, George Konidaris, 2010 .

Convergence of Indirect Adaptive Asynchronous Value Iteration Algorithms

Andrew G. Barto, Vijaykumar Gullapalli, A. Barto, 1993, NIPS.

Reinforcement learning

Andrew G. Barto, A. Barto, 1998 .

CST: Constructing Skill Trees by Demonstration

Scott Kuindersma, Roderic A. Grupen, George Konidaris, 2011, ICML 2011.

Adaptive Control Processes

Sanjoy Dasgupta, Hannu Toivonen, Pieter Abbeel, 2010, Encyclopedia of Machine Learning and Data Mining.

A Unified View

Richard S. Sutton, Andrew G. Barto, R. Sutton, 1998 .

Planning and Learning

Richard S. Sutton, Andrew G. Barto, R. Sutton, 1998 .

Novelty or Surprise?

A. Barto, G. Baldassarre, M. Mirolli, 2013, Front. Psychol..

Distributed motor commands in the limb premotor network

A. Barto, J. Houk, J. Keifer, 1993, Trends in Neurosciences.

Local Graph Partitioning as a Basis for Generating Temporally-Extended Actions in Reinforcement Learning

Alicia P. Wolfe, A. Barto, Özgür Simsek, 2005 .

Reinforcement Learning in Large, High‐Dimensional State Spaces

A. Barto, Warrren B Powell, J. Si, 2004 .

Repairing Disengagement With Non-Invasive Interventions

Beverly Park Woolf, Sridhar Mahadevan, Andrew G. Barto, 2007, AIED.

Robust Reinforcement Learning for Heating, Ventilation, and Air Conditioning Control of Buildings

Jennie Si, Don Wunsch, Andrew G. Barto, 2004 .

Causal Graph Based Decomposition of Factored MDPs

Andrew G. Barto, Anders Jonsson, A. Barto, 2006, J. Mach. Learn. Res..

Accelerating Reinforcement Learning through the Discovery of Useful Subgoals

Andrew G. Barto, Amy McGovern, A. Barto, 2001 .

Genetic Programming for Reward Function Search

Lee Spector, Scott Niekum, Andrew G. Barto, 2010, IEEE Transactions on Autonomous Mental Development.

A Predictive Switching Model of Cerebellar Movement Control

James C. Houk, Andrew G. Barto, A. Barto, 1995, NIPS.

Learning at the level of synergies for a robot weightlifter

Michael T. Rosenstein, Andrew G. Barto, Richard E. A. Van Emmerik, 2006, Robotics Auton. Syst..

Optimal Control Methods for Simulating the Perception of Causality in Young Infants

A. Barto, M. Schlesinger, 2020, Proceedings of the Twenty First Annual Conference of the Cognitive Science Society.

Learning and incremental dynamic programming

Andrew G. Barto, A. Barto, 1991, Behavioral and Brain Sciences.

Reinforcement Learning and Its Relationship to Supervised Learning

Warren B. Powell, Jennie Si, Andrew G. Barto, 2004 .

Lyapunov-Constrained Action Sets for Reinforcement Learning

Andrew G. Barto, Theodore J. Perkins, A. Barto, 2001, ICML.

A Cerebellar Model of Timing and Prediction in the Control of Reaching

James C. Houk, Nathan Sitkoff, Andrew H. Fagg, 1999, Neural Computation.

More models of the cerebellum

James C. Houk, Andrew G. Barto, A. Barto, 1996 .

Active Learning of Dynamic Bayesian Networks in Markov Decision Processes

Andrew G. Barto, Anders Jonsson, A. Barto, 2007, SARA.

Online Bayesian changepoint detection for articulated motion models

Christopher G. Atkeson, Scott Niekum, Andrew G. Barto, 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

Cooperative Interaction of Self-Interested Neuron-Like Processing Units

Andrew G. Barto, A. Barto, 1989 .

Automated Aircraft Recovery via Reinforcement Learning: Initial Experiments

David G. Ward, Andrew G. Barto, Jeffrey F. Monaco, 1997, NIPS.

A Note on Pattern Reproduction in Tessellation Structures

Andrew G. Barto, A. Barto, 1978, J. Comput. Syst. Sci..

Heuristic Search in Infinite State Spaces Guided by Lyapunov Analysis

Andrew G. Barto, Theodore J. Perkins, A. Barto, 2001, IJCAI.

Reinforcement learning in motor control

Andrew G. Barto, A. Barto, 1998 .

On Ensuring that Intelligent Machines Are Well-Behaved pdf

Bruno Castro da Silva, Andrew G. Barto, Philip S. Thomas, 2017, ArXiv.

An Adaptive Sensorimotor Network Inspired by the Anatomy and Physiology

James C. Houk, Andrew G. Barto, Satinder Singh, 1989 .

Cellular automata as models of natural systems

Andrew G. Barto, A. Barto, 1975 .

Skill Chaining : Skill Discovery in Continuous Domains

A. Barto, G. Konidaris, 2009 .

DISCRETE AND CONTINUOUS MODELS

Andrew G. Barto, A. Barto, 1978 .

Learning dynamic arm motions for postural recovery

Scott Kuindersma, Roderic A. Grupen, Andrew G. Barto, 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

Machine Learning for Subproblem Selection

Andrew G. Barto, Theodore J. Perkins, Robert Moll, 2000, ICML.

Learned Subproblem Selection Techniques for Combinatorial Optimization

A. Barto, T. Perkins, R. Moll, 1999 .

Learning to exploit dynamics for robot motor coordination

Michael T. Rosenstein, Andrew G. Barto, A. Barto, 2003 .

Reinforcement and Local Searc: A Case Study TITLE2:

R. Sutton, A. Barto, T. Perkins, 1997 .

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

Yariv Z. Levy, Jerrold S. Meyer, A. Barto, 2013, Front. Psychiatry.

Linear Least-Squares algorithms for temporal difference learning

Steven J. Bradtke, A. Barto, S. Bradtke, 2004, Machine Learning.

Errata Preface Recent Advances in Hierarchical Reinforcement Learning

A. Barto, S. Mahadevan, 2003 .

Reinforcement Learning in Artificial Intelligence

R. Sutton, A. Barto, 1997 .

or ces to Create Useful Concepts for Evaluating States

A. Barto, R. Yee, P. Utgoff, 1990 .

Intrinsically Motivated Machines

Richard L. Lewis, A. Barto, Satinder Singh, 2011 .

Imitation Learning in The Game of Go with Joseki Options

A. Barto, Will Dabney, A. McGovern, 2010 .

A Agent Environment StatesActions Rewards Critic B Agent Internal Environment Rewards Critic External Environment Sensations StatesDecisions Actions " Organism " Figure 1

A. Barto, 2004 .

Effect on movement selection of an evolving sensory representation: A multiple controller model of skill acquisition

A. Barto, Ashvin Shah, 2009, Brain Research.

Robot Learning : Some Recent Examples

A. Barto, G. Konidaris, S. Kuindersma, 2013 .

Autonomous Hierarchical Skill Acquisition in Factored MDPs

Christopher M. Vigorito, A. Barto, 2008 .

1 Supervised Actor-Critic Reinforcement Learning

A. Barto, M. Rosenstein, 2007 .

Where Do Rewards Come From ? 1

Richard L. Lewis, A. Barto, Satinder Singh, 2010 .

Cortical involvement in the recruitment of wrist muscles.

A. Barto, A. Fagg, Ashvin Shah, 2004, Journal of neurophysiology.

Game-theoretic cooperativity in networks of self-interested units

Andrew G. Barto, A. Barto, 1987 .

Reinforcement Learning: Connections, Surprises, Challenges

A. Barto, 2019 .

Learning grounded finite-state representations from unstructured demonstrations

Scott Niekum, Andrew G. Barto, George Konidaris, 2015, Int. J. Robotics Res..

Learning and control in a chaotic system

A. Barto, J. Randløv, 1999 .

Learning Articulation Changepoint Models from Demonstration

C. Atkeson, A. Barto, S. Niekum, 2014 .

Shaping as a method for accelerating reinforcement learning

Andrew G. Barto, Vijaykumar Gullapalli, A. Barto, 1992, Proceedings of the 1992 IEEE International Symposium on Intelligent Control.

Learning as Hillclimbing in Weight Space

Andrew G. Barto, A. Barto, 2020 .

Toward the Autonomous Acquisition of Robot Skill Hierarchies

A. Barto, G. Konidaris, 2009 .

Betweenness Centrality as a Basis for Forming Skills Özgür Şimşek

Andrew G. Barto, A. Barto, 2007 .

Statistical Machine Learning for Large-Scale Optimization Contributors

Thomas G. Dietterich, Wray L. Buntine, R. Impagliazzo, 2022 .

Designing Adaptive Sensing Policies for Meteorological Phenomena via Spectral Analysis of Radar Images

Bruno C. da Silva, A. Barto, B. B. D. Silva, 2012 .

Adaptive Real-Time Dynamic Programming

Andrew G. Barto, A. Barto, 2017, Encyclopedia of Machine Learning and Data Mining.

Homomorphisms : An Algebraic Approach to Abstraction in Semi-Markov Decision Processes

A. Barto, Balaraman Ravindran, 2003 .

Learning as hill-climbing in weight space

Andrew G. Barto, A. Barto, 1998 .

Reinforcement Learning and Local Search: a Case Study Reinforcement Learning and Local Search: a Case Study

A. Barto, T. Perkins, Richard SuttonCMPSCI, 1997 .

Structurally Invariant Linear Models of Structurally Varying Linear Systems

A. Barto, 1978 .

Adaptive Real-Time Dynamic Programming

Andrew G. Barto, A. Barto, 2017, Encyclopedia of Machine Learning and Data Mining.

An approach to learning control surfaces by connectionist systems

Andrew G. Barto, A. Barto, 1990 .

Motor programs and sensorimotor integration

Thomas Sinkjær, James C. Houk, Andrew G. Barto, 1990 .

Optimal learning: computational procedures for bayes-adaptive markov decision processes

Andrew G. Barto, Michael O. Duff, A. Barto, 2002 .

6. The Linear Programming Approach to Approximate Dynamic Programming

Jennie Si, Andy Barto, Warren Powell and, 2012 .

Approximate Dynamic Programming

Geoffrey I. Webb, Stephen Grossberg, Mauro Birattari, 2011 .

DISCRETE AND CONTINUOUS MODELS

A. Barto, 1978 .

BPTT and DAC — A Common Framework for Comparison

Jennie Si, Danil V. Prokhorov, Warren Buckler Powell, 2012 .

Sequential Decision Probelms and Neural Networks

Richard S. Sutton, Andrew G. Barto, Christopher J. C. H. Watkins, 1989, NIPS.

Reinforcement Learning: Connections, Surprises, and Challenge

Andrew G. Barto, A. Barto, 2019, AI Mag..

Proceedings of the IJCNN Winter Meeting, IEEE, 1990, Washington, DC, USA

Thomas Sinkjær, James C. Houk, Andrew G. Barto, 1990 .

Near‐Optimal Control Via Reinforcement Learning and Hybridization

Jennie Si, Augustine O. Esogbue, Warren Buckler Powell, 2012 .

Fagg, Barto & Houk: Learning to Reach via Corrective Movements; Tenth Yale Workshop on Adaptive and Learning Systems Learning to Reach via Corrective Movements

A. Barto, J. Houk, MovementsAndrew H. Fagg, 1998 .

Simulation Experiments with Goal-Seeking Adaptive Elements.

A. Barto, 1984 .

On the Computational Economics of Reinforcement Learning

Andrew G. Barto, Satinder Singh, Satinder Singh, 1991 .

Introduction to Reinforcement Learning

Richard S. Sutton, Andrew G. Barto, R. Sutton, 1998 .

Paying attention to what matters: observation abstraction in partially observable environments

Alicia P. Wolfe, Andrew G. Barto, Alicia Peregrin Wolfe, 2010 .

Deictic Option Schemas

Balaraman Ravindran, Andrew G. Barto, Vimal Mathew, 2007, IJCAI.

A causal approach to hierarchical decomposition in reinforcement learning

Anders Jonsson, Andrew G. Barto, A. Barto, 2006 .

Reinforcement learning, efficient coding, and the statistics of natural tasks

Alec Solway, Ari Weinstein, Matthew Botvinick, 2015, Current Opinion in Behavioral Sciences.

Intrinsically Motivated Reinforcement Learning: A Promising Framework for Developmental Robot Learning

Andrew G. Barto, George Konidaris, Andrew Stout, 2005 .

Chapter 12 Time-Derivative Models of Pavlovian Reinforcement

R. Sutton, A. Barto, 1990 .

Preventing undesirable behavior of intelligent machines

Yuriy Brun, Philip S Thomas, Bruno Castro da Silva, 2019, Science.

Book Reviews

R. Sutton, A. Barto, 1999, Journal of Cognitive Neuroscience.

Controlling a Nonlinear Spring-Mass System with a Cerebellar Model

James C. Houk, Andrew G. Barto, Jay T. Buckingham, 1994 .

A Dual Process Account of Coarticulation in Motor Skill Acquisition

Ashvin Shah, Andrew H Fagg, Andrew G Barto, 2013, Journal of motor behavior.

Improving Elevator Performance Using Reinforcement Learning

Andrew G. Barto, Robert H. Crites, A. Barto, 1995, NIPS.

Editorial: Intrinsically Motivated Open-Ended Learning in Autonomous Robots

Pierre-Yves Oudeyer, Gianluca Baldassarre, Andrew Barto, 2020, Frontiers in Neurorobotics.

Homomorphisms : An Algebraic Approach to Abstraction in Semi-Markov Decision Processes

A. Barto, Balaraman Ravindran, 2003 .

Reinforcement learning with analogue memristor arrays

A. Barto, J. Yang, J. Strachan, 2019, Nature Electronics.

Reinforcement learning with analogue memristor arrays

Peng Lin, Peng Yan, Qinru Qiu, 2019, Nature Electronics.

Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)

Jennie Si, Andrew G. Barto, Don Wunsch, 2004 .

Using relative novelty to identify useful temporal abstractions in reinforcement learning

Andrew G. Barto, Özgür Simsek, A. Barto, 2004, ICML.

Reinforcement Learning is Direct Adaptive Optimal Control

R. Sutton, Ronald J. Williams, A. Barto, 1992, 1991 American Control Conference.

Stochastic Scheduling and Planning Using Reinforcement Learning

Andrew G Barto, A. Barto, 2000 .

Learning parameterized motor skills on a humanoid robot

Bruno Castro da Silva, Andrew G. Barto, George Konidaris, 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

Incremental Semantically Grounded Learning from Demonstration

Scott Niekum, Andrew G. Barto, Sachin Chitta, 2013, Robotics: Science and Systems.

Skill Characterization Based on Betweenness

Andrew G. Barto, Özgür Simsek, A. Barto, 2008, NIPS.

Where Do Rewards Come From

Richard L. Lewis, Satinder Singh, A. Barto, 2009 .

Experience-Based Creativity

A. Barto, R. Yee, J. Holland, 1991 .

Sensorimotor abstraction selection for efficient, autonomous robot skill acquisition

G. Konidaris, A. Barto, A. Barto, 2008, 2008 7th IEEE International Conference on Development and Learning.

Learning Skills in Reinforcement Learning Using Relative Novelty

Andrew G. Barto, Özgür Simsek, A. Barto, 2005, SARA.

Transfer in Reinforcement Learning via Shared Features Citation

Andrew G. Barto, George Konidaris, Ilya Scheidwasser, 2012 .

Local Graph Partitioning as a Basis for Generating Temporally-Extended Actions in Reinforcement Learning

Alicia P. Wolfe, A. Barto, Özgür Simsek, 2005 .

Gradient Ascent Critic Optimization

A. Barto, Will Dabney, 2010 .

Introduction to Reinforcement Learning

A. Barto, Richard S. Sutton, R. S. Sutton, 1998 .

Intrinsically Motivated Reinforcement Learning

Satinder Singh, A. Barto, N. Chentanez, 2004, NIPS.

Intrinsic Motivation and Reinforcement Learning

A. Barto, 2013, Intrinsically Motivated Learning in Natural and Artificial Systems.

Reinforcement Learning: An Introduction

P. Read Montague, A. Barto, P. Montague, 2005, IEEE Transactions on Neural Networks.

A computational model of muscle recruitment for wrist movements.

Ashvin Shah, Andrew H Fagg, Andrew G Barto, 2002, Journal of neurophysiology.

Some Recent Applications of Reinforcement Learning

R. S. Sutton, P. S. Thomas, R. Sutton, 2017 .

Learning and Sequential Decision Making

A. Barto, J. W. Moore, R. S. Sutton, 1989 .

The Complexity of Learning Separable ceteris paribus Preferences

A. Barto, Balaraman Ravindran, 2009, IJCAI.

In : Models of Information Processing in the Basal Ganglia

Joel L. Davis, A. Barto, J. Houk, 2008 .

Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)

A. Barto, Warrren B Powell, J. Si, 2004 .