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Andrew G. Barto
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- affiliation: Department of Computer Science, University of Massachusetts Amherst
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2020 – today
- 2021
- [j30]Andrew G. Barto
, Richard S. Sutton, Charles W. Anderson
Looking Back on the Actor-Critic Architecture. IEEE Trans. Syst. Man Cybern. Syst. 51(1): 40-50 (2021)
2010 – 2019
- 2019
- [j29]Andrew G. Barto:
Reinforcement Learning: Connections, Surprises, and Challenge. AI Mag. 40(1): 3-15 (2019) - [j28]Vieri Giuliano Santucci
Editorial: Intrinsically Motivated Open-Ended Learning in Autonomous Robots. Frontiers Neurorobotics 13: 115 (2019) - 2017
- [r2]Andrew G. Barto:
Adaptive Real-Time Dynamic Programming. Encyclopedia of Machine Learning and Data Mining 2017: 20-23 - [i1]Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto, Emma Brunskill:
On Ensuring that Intelligent Machines Are Well-Behaved. CoRR abs/1708.05448 (2017) - 2015
- [j27]Scott Niekum, Sarah Osentoski, George Dimitri Konidaris, Sachin Chitta, Bhaskara Marthi, Andrew G. Barto:
Learning grounded finite-state representations from unstructured demonstrations. Int. J. Robotics Res. 34(2): 131-157 (2015) - [c71]Scott Niekum, Sarah Osentoski, Christopher G. Atkeson, Andrew G. Barto:
Online Bayesian changepoint detection for articulated motion models. ICRA 2015: 1468-1475 - 2014
- [j26]Alec Solway, Carlos Diuk, Natalia Córdova, Debbie Yee, Andrew G. Barto, Yael Niv, Matthew M. Botvinick:
Optimal Behavioral Hierarchy. PLoS Comput. Biol. 10(8) (2014) - [j25]Andrew G. Barto:
Commentary on Utility and Bounds. Top. Cogn. Sci. 6(2): 338-341 (2014) - [c70]Bruno Castro da Silva, George Dimitri Konidaris, Andrew G. Barto:
Active Learning of Parameterized Skills. ICML 2014: 1737-1745 - [c69]Bruno Castro da Silva, Gianluca Baldassarre
Learning parameterized motor skills on a humanoid robot. ICRA 2014: 5239-5244 - 2013
- [j24]Scott Kuindersma, Roderic A. Grupen, Andrew G. Barto:
Variable risk control via stochastic optimization. Int. J. Robotics Res. 32(7): 806-825 (2013) - [c68]Scott Niekum, Sachin Chitta, Andrew G. Barto, Bhaskara Marthi, Sarah Osentoski:
Incremental Semantically Grounded Learning from Demonstration. Robotics: Science and Systems 2013 - [p2]Andrew G. Barto, George Dimitri Konidaris, Christopher M. Vigorito:
Behavioral Hierarchy: Exploration and Representation. Computational and Robotic Models of the Hierarchical Organization of Behavior 2013: 13-46 - [p1]Andrew G. Barto:
Intrinsic Motivation and Reinforcement Learning. Intrinsically Motivated Learning in Natural and Artificial Systems 2013: 17-47 - 2012
- [j23]George Dimitri Konidaris, Scott Kuindersma, Roderic A. Grupen, Andrew G. Barto:
Robot learning from demonstration by constructing skill trees. Int. J. Robotics Res. 31(3): 360-375 (2012) - [j22]George Dimitri Konidaris, Ilya Scheidwasser, Andrew G. Barto:
Transfer in Reinforcement Learning via Shared Features. J. Mach. Learn. Res. 13: 1333-1371 (2012) - [c67]William Dabney, Andrew G. Barto:
Adaptive Step-Size for Online Temporal Difference Learning. AAAI 2012: 872-878 - [c66]Bruno Castro da Silva, Andrew G. Barto:
TD-DeltaPi: A Model-Free Algorithm for Efficient Exploration. AAAI 2012: 886-892 - [c65]Philip S. Thomas, Andrew G. Barto:
Motor primitive discovery. ICDL-EPIROB 2012: 1-8 - [c64]Bruno Castro da Silva, George Dimitri Konidaris, Andrew G. Barto:
Learning Parameterized Skills. ICML 2012 - [c63]Scott Niekum, Sarah Osentoski, George Dimitri Konidaris, Andrew G. Barto:
Learning and generalization of complex tasks from unstructured demonstrations. IROS 2012: 5239-5246 - [c62]Scott Kuindersma, Roderic A. Grupen, Andrew G. Barto:
Variational Bayesian Optimization for Runtime Risk-Sensitive Control. Robotics: Science and Systems 2012 - 2011
- [c61]George Dimitri Konidaris, Scott Kuindersma, Roderic A. Grupen, Andrew G. Barto:
Autonomous Skill Acquisition on a Mobile Manipulator. AAAI 2011: 1468-1473 - [c60]Scott Niekum, Andrew G. Barto:
Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery. Lifelong Learning 2011 - [c59]Scott Niekum, Lee Spector, Andrew G. Barto:
Evolution of reward functions for reinforcement learning. GECCO (Companion) 2011: 177-178 - [c58]Scott Kuindersma, Roderic A. Grupen, Andrew G. Barto:
Learning dynamic arm motions for postural recovery. Humanoids 2011: 7-12 - [c57]Philip S. Thomas, Andrew G. Barto:
Conjugate Markov Decision Processes. ICML 2011: 137-144 - [c56]Scott Niekum, Andrew G. Barto:
Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery. NIPS 2011: 1818-1826 - 2010
- [j21]Satinder Singh, Richard L. Lewis, Andrew G. Barto, Jonathan Sorg:
Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective. IEEE Trans. Auton. Ment. Dev. 2(2): 70-82 (2010) - [j20]Scott Niekum, Andrew G. Barto, Lee Spector:
Genetic Programming for Reward Function Search. IEEE Trans. Auton. Ment. Dev. 2(2): 83-90 (2010) - [j19]Christopher M. Vigorito, Andrew G. Barto:
Intrinsically Motivated Hierarchical Skill Learning in Structured Environments. IEEE Trans. Auton. Ment. Dev. 2(2): 132-143 (2010) - [c55]Andrew Stout, Andrew G. Barto:
Competence progress intrinsic motivation. ICDL 2010: 257-262 - [c54]George Dimitri Konidaris, Scott Kuindersma, Andrew G. Barto, Roderic A. Grupen:
Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories. NIPS 2010: 1162-1170 - [r1]Andrew G. Barto:
Adaptive Real-Time Dynamic Programming. Encyclopedia of Machine Learning 2010: 19-22
2000 – 2009
- 2009
- [c53]George Dimitri Konidaris, Andrew G. Barto:
Efficient Skill Learning using Abstraction Selection. IJCAI 2009: 1107-1112 - [c52]George Dimitri Konidaris, Andrew G. Barto:
Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining. NIPS 2009: 1015-1023 - 2008
- [c51]Christopher M. Vigorito, Andrew G. Barto:
Hierarchical Representations of Behavior for Efficient Creative Search. AAAI Spring Symposium: Creative Intelligent Systems 2008: 135-141 - [c50]Özgür Simsek, Andrew G. Barto:
Skill Characterization Based on Betweenness. NIPS 2008: 1497-1504 - 2007
- [j18]Andrew G. Barto:
Temporal difference learning. Scholarpedia 2(11): 1604 (2007) - [c49]Ivon Arroyo, Kimberly Ferguson, Jeffrey Johns, Toby Dragon, Hasmik Meheranian, Don Fisher, Andrew G. Barto, Sridhar Mahadevan, Beverly Park Woolf:
Repairing Disengagement With Non-Invasive Interventions. AIED 2007: 195-202 - [c48]George Dimitri Konidaris, Andrew G. Barto:
Building Portable Options: Skill Transfer in Reinforcement Learning. IJCAI 2007: 895-900 - [c47]Balaraman Ravindran, Andrew G. Barto, Vimal Mathew:
Deictic Option Schemas. IJCAI 2007: 1023-1028 - [c46]Anders Jonsson, Andrew G. Barto:
Active Learning of Dynamic Bayesian Networks in Markov Decision Processes. SARA 2007: 273-284 - [c45]Christopher M. Vigorito, Deepak Ganesan, Andrew G. Barto:
Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks. SECON 2007: 21-30 - 2006
- [j17]Anders Jonsson, Andrew G. Barto:
Causal Graph Based Decomposition of Factored MDPs. J. Mach. Learn. Res. 7: 2259-2301 (2006) - [j16]Michael T. Rosenstein, Andrew G. Barto, Richard E. A. Van Emmerik:
Learning at the level of synergies for a robot weightlifter. Robotics Auton. Syst. 54(8): 706-717 (2006) - [c44]Alicia P. Wolfe, Andrew G. Barto:
Decision Tree Methods for Finding Reusable MDP Homomorphisms. AAAI 2006: 530-535 - [c43]George Dimitri Konidaris, Andrew G. Barto:
Autonomous shaping: knowledge transfer in reinforcement learning. ICML 2006: 489-496 - [c42]Özgür Simsek
An intrinsic reward mechanism for efficient exploration. ICML 2006: 833-840 - [c41]Kimberly Ferguson, Ivon Arroyo, Sridhar Mahadevan, Beverly Park Woolf, Andrew G. Barto:
Improving Intelligent Tutoring Systems: Using Expectation Maximization to Learn Student Skill Levels. Intelligent Tutoring Systems 2006: 453-462 - [c40]George Dimitri Konidaris, Andrew G. Barto:
An Adaptive Robot Motivational System. SAB 2006: 346-356 - 2005
- [c39]Anders Jonsson, Andrew G. Barto:
A causal approach to hierarchical decomposition of factored MDPs. ICML 2005: 401-408 - [c38]Özgür Simsek
Identifying useful subgoals in reinforcement learning by local graph partitioning. ICML 2005: 816-823 - [c37]Özgür Simsek
Learning Skills in Reinforcement Learning Using Relative Novelty. SARA 2005: 367-374 - 2004
- [c36]Michael T. Rosenstein, Andrew G. Barto:
Reinforcement learning with supervision by a stable controller. ACC 2004: 4517-4522 - [c35]Özgür Simsek, Andrew G. Barto:
Using relative novelty to identify useful temporal abstractions in reinforcement learning. ICML 2004 - [c34]Satinder Singh, Andrew G. Barto, Nuttapong Chentanez:
Intrinsically Motivated Reinforcement Learning. NIPS 2004: 1281-1288 - 2003
- [j15]Andrew G. Barto, Sridhar Mahadevan:
Recent Advances in Hierarchical Reinforcement Learning. Discret. Event Dyn. Syst. 13(1-2): 41-77 (2003) - [j14]Andrew G. Barto, Sridhar Mahadevan:
Recent Advances in Hierarchical Reinforcement Learning. Discret. Event Dyn. Syst. 13(4): 341-379 (2003) - [c33]Balaraman Ravindran, Andrew G. Barto:
Relativized Options: Choosing the Right Transformation. ICML 2003: 608-615 - [c32]Balaraman Ravindran, Andrew G. Barto:
SMDP Homomorphisms: An Algebraic Approach to Abstraction in Semi-Markov Decision Processes. IJCAI 2003: 1011-1018 - 2002
- [j13]Michael Kositsky, Andrew G. Barto:
The emergence of movement units through learning with noisy efferent signals and delayed sensory feedback. Neurocomputing 44-46: 889-895 (2002) - [j12]Theodore J. Perkins, Andrew G. Barto:
Lyapunov Design for Safe Reinforcement Learning. J. Mach. Learn. Res. 3: 803-832 (2002) - [j11]Amy McGovern, J. Eliot B. Moss, Andrew G. Barto:
Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts. Mach. Learn. 49(2-3): 141-160 (2002) - [c31]Marc Pickett, Andrew G. Barto:
PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning. ICML 2002: 506-513 - [c30]Balaraman Ravindran
Model Minimization in Hierarchical Reinforcement Learning. SARA 2002: 196-211 - 2001
- [c29]Amy McGovern, Andrew G. Barto:
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density. ICML 2001: 361-368 - [c28]Theodore J. Perkins, Andrew G. Barto:
Lyapunov-Constrained Action Sets for Reinforcement Learning. ICML 2001: 409-416 - [c27]Theodore J. Perkins, Andrew G. Barto:
Heuristic Search in Infinite State Spaces Guided by Lyapunov Analysis. IJCAI 2001: 242-247 - [c26]Michael T. Rosenstein, Andrew G. Barto:
Robot Weightlifting By Direct Policy Search. IJCAI 2001: 839-846 - [c25]Michael Kositsky, Andrew G. Barto:
The Emergence of Multiple Movement Units in the Presence of Noise and Feedback Delay. NIPS 2001: 43-50 - 2000
- [c24]Robert Moll, Theodore J. Perkins, Andrew G. Barto:
Machine Learning for Subproblem Selection. ICML 2000: 615-622 - [c23]Jette Randløv, Andrew G. Barto, Michael T. Rosenstein:
Combining Reinforcement Learning with a Local Control Algorithm. ICML 2000: 775-782 - [c22]Anders Jonsson, Andrew G. Barto:
Automated State Abstraction for Options using the U-Tree Algorithm. NIPS 2000: 1054-1060
1990 – 1999
- 1999
- [j10]Andrew G. Barto, Andrew H. Fagg, Nathan Sitkoff, James C. Houk:
A Cerebellar Model of Timing and Prediction in the Control of Reaching. Neural Comput. 11(3): 565-594 (1999) - 1998
- [b1]Richard S. Sutton, Andrew G. Barto:
Reinforcement learning - an introduction. Adaptive computation and machine learning, MIT Press 1998, ISBN 978-0-262-19398-6, pp. I-XVIII, 1-322 - [j9]Robert H. Crites, Andrew G. Barto:
Elevator Group Control Using Multiple Reinforcement Learning Agents. Mach. Learn. 33(2-3): 235-262 (1998) - [j8]Richard S. Sutton, Andrew G. Barto:
Reinforcement Learning: An Introduction. IEEE Trans. Neural Networks 9(5): 1054-1054 (1998) - [c21]Robert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton:
Learning Instance-Independent Value Functions to Enhance Local Search. NIPS 1998: 1017-1023 - 1997
- [c20]Andrew H. Fagg, Nathan Sitkoff, Andrew G. Barto, James C. Houk:
A model of cerebellar learning for control of arm movements using muscle synergies. CIRA 1997: 6-12 - [c19]Andrew H. Fagg, Nathan Sitkoff, Andrew G. Barto, James C. Houk:
Cerebellar learning for control of a two-link arm in muscle space. ICRA 1997: 2638-2644 - [c18]Jeffrey F. Monaco, David G. Ward, Andrew G. Barto:
Automated Aircraft Recovery via Reinforcement Learning: Initial Experiments. NIPS 1997: 1022-1028 - 1996
- [j7]Steven J. Bradtke, Andrew G. Barto:
Linear Least-Squares Algorithms for Temporal Difference Learning. Mach. Learn. 22(1-3): 33-57 (1996) - [c17]Ron Papka, James P. Callan, Andrew G. Barto:
Text-Based Information Retrieval Using Exponentiated Gradient Descent. NIPS 1996: 3-9 - [c16]Michael O. Duff, Andrew G. Barto:
Local Bandit Approximation for Optimal Learning Problems. NIPS 1996: 1019-1025 - [c15]Eric A. Hansen, Andrew G. Barto, Shlomo Zilberstein:
Reinforcement Learning for Mixed Open-loop and Closed-loop Control. NIPS 1996: 1026-1032 - 1995
- [j6]Andrew G. Barto, Steven J. Bradtke, Satinder P. Singh:
Learning to Act Using Real-Time Dynamic Programming. Artif. Intell. 72(1-2): 81-138 (1995) - [c14]Andrew G. Barto, James C. Houk:
A Predictive Switching Model of Cerebellar Movement Control. NIPS 1995: 138-144 - [c13]Robert H. Crites, Andrew G. Barto:
Improving Elevator Performance Using Reinforcement Learning. NIPS 1995: 1017-1023 - 1994
- [c12]Vijaykumar Gullapalli, Andrew G. Barto, Roderic A. Grupen:
Learning Admittance Mappings for Force-Guided Assembly. ICRA 1994: 2633-2638 - [c11]Robert H. Crites, Andrew G. Barto:
An Actor/Critic Algorithm that is Equivalent to Q-Learning. NIPS 1994: 401-408 - 1993
- [c10]Satinder Singh, Andrew G. Barto, Roderic A. Grupen, Christopher I. Connolly:
Robust Reinforcement Learning in Motion Planning. NIPS 1993: 655-662 - [c9]Andrew G. Barto, Michael O. Duff:
Monte Carlo Matrix Inversion and Reinforcement Learning. NIPS 1993: 687-694 - [c8]Vijaykumar Gullapalli, Andrew G. Barto:
Convergence of Indirect Adaptive Asynchronous Value Iteration Algorithms. NIPS 1993: 695-702 - [c7]Robert A. Jacobs, Michael I. Jordan, Andrew G. Barto:
Task Decompostiion Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. Machine Learning: From Theory to Applications 1993: 175-202 - 1992
- [c6]Vijaykumar Gullapalli, Roderic A. Grupen, Andrew G. Barto:
Learning reactive admittance control. ICRA 1992: 1475-1480 - 1991
- [j5]N. E. Berthier, Andrew G. Barto, J. W. Moore:
Linear systems analysis of the relationship between firing of deep cerebellar neurons and the classically conditioned nictitating membrane response in rabbits. Biol. Cybern. 65(2): 99-105 (1991) - [j4]Robert A. Jacobs, Michael I. Jordan, Andrew G. Barto:
Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. Cogn. Sci. 15(2): 219-250 (1991) - [c5]N. E. Berthier, Satinder P. Singh, Andrew G. Barto, James C. Houk:
A Cortico-Cerebellar Model that Learns to Generate Distributed Motor Commands to Control a Kinematic Arm. NIPS 1991: 611-618 - 1990
- [c4]Richard C. Yee, Sharad Saxena, Paul E. Utgoff, Andrew G. Barto:
Explaining Temporal Differences to Create Useful Concepts for Evaluating States. AAAI 1990: 882-888 - [c3]T. Sinkjaer, C. H. Wu, Andrew G. Barto, James C. Houk:
Cerebellar control of endpoint position-a simulation model. IJCNN 1990: 705-710
1980 – 1989
- 1989
- [c2]Andrew G. Barto, Richard S. Sutton, Christopher J. C. H. Watkins:
Sequential Decision Probelms and Neural Networks. NIPS 1989: 686-693 - 1985
- [j3]Andrew G. Barto, P. Anandan:
Pattern-recognizing stochastic learning automata. IEEE Trans. Syst. Man Cybern. 15(3): 360-375 (1985) - [c1]Oliver G. Selfridge, Richard S. Sutton, Andrew G. Barto:
Training and Tracking in Robotics. IJCAI 1985: 670-672 - 1983
- [j2]Andrew G. Barto, Richard S. Sutton, Charles W. Anderson
Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern. 13(5): 834-846 (1983)
1970 – 1979
- 1978
- [j1]Andrew G. Barto:
A Note on Pattern Reproduction in Tessellation Structures. J. Comput. Syst. Sci. 16(3): 445-455 (1978)
Coauthor Index
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