Andrew G. Barto

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60EEÖzgür Simsek, Andrew G. Barto: Skill Characterization Based on Betweenness. NIPS 2008: 1497-1504
59 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
58EEBalaraman Ravindran, Andrew G. Barto, Vimal Mathew: Deictic Option Schemas. IJCAI 2007: 1023-1028
57EEGeorge Konidaris, Andrew G. Barto: Building Portable Options: Skill Transfer in Reinforcement Learning. IJCAI 2007: 895-900
56EEAnders Jonsson, Andrew G. Barto: Active Learning of Dynamic Bayesian Networks in Markov Decision Processes. SARA 2007: 273-284
55EEChristopher M. Vigorito, Deepak Ganesan, Andrew G. Barto: Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks. SECON 2007: 21-30
54EEAndrew G. Barto: Temporal difference learning. Scholarpedia 2(11): 1604 (2007)
53 Alicia P. Wolfe, Andrew G. Barto: Decision Tree Methods for Finding Reusable MDP Homomorphisms. AAAI 2006
52EEGeorge Konidaris, Andrew G. Barto: Autonomous shaping: knowledge transfer in reinforcement learning. ICML 2006: 489-496
51EEÖzgür Simsek, Andrew G. Barto: An intrinsic reward mechanism for efficient exploration. ICML 2006: 833-840
50EEKimberly 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
49EEGeorge Konidaris, Andrew G. Barto: An Adaptive Robot Motivational System. SAB 2006: 346-356
48EEAnders Jonsson, Andrew G. Barto: Causal Graph Based Decomposition of Factored MDPs. Journal of Machine Learning Research 7: 2259-2301 (2006)
47EEMichael T. Rosenstein, Andrew G. Barto, Richard E. A. Van Emmerik: Learning at the level of synergies for a robot weightlifter. Robotics and Autonomous Systems 54(8): 706-717 (2006)
46EEAnders Jonsson, Andrew G. Barto: A causal approach to hierarchical decomposition of factored MDPs. ICML 2005: 401-408
45EEÖzgür Simsek, Alicia P. Wolfe, Andrew G. Barto: Identifying useful subgoals in reinforcement learning by local graph partitioning. ICML 2005: 816-823
44EEÖzgür Simsek, Andrew G. Barto: Learning Skills in Reinforcement Learning Using Relative Novelty. SARA 2005: 367-374
43EEÖzgür Simsek, Andrew G. Barto: Using relative novelty to identify useful temporal abstractions in reinforcement learning. ICML 2004
42EESatinder P. Singh, Andrew G. Barto, Nuttapong Chentanez: Intrinsically Motivated Reinforcement Learning. NIPS 2004
41 Balaraman Ravindran, Andrew G. Barto: Relativized Options: Choosing the Right Transformation. ICML 2003: 608-615
40 Balaraman Ravindran, Andrew G. Barto: SMDP Homomorphisms: An Algebraic Approach to Abstraction in Semi-Markov Decision Processes. IJCAI 2003: 1011-1018
39EEAndrew G. Barto, Sridhar Mahadevan: Recent Advances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems 13(1-2): 41-77 (2003)
38EEAndrew G. Barto, Sridhar Mahadevan: Recent Advances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems 13(4): 341-379 (2003)
37 Marc Pickett, Andrew G. Barto: PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning. ICML 2002: 506-513
36EEBalaraman Ravindran, Andrew G. Barto: Model Minimization in Hierarchical Reinforcement Learning. SARA 2002: 196-211
35EETheodore J. Perkins, Andrew G. Barto: Lyapunov Design for Safe Reinforcement Learning. Journal of Machine Learning Research 3: 803-832 (2002)
34 Amy McGovern, J. Eliot B. Moss, Andrew G. Barto: Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts. Machine Learning 49(2-3): 141-160 (2002)
33EEMichael 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)
32 Amy McGovern, Andrew G. Barto: Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density. ICML 2001: 361-368
31 Theodore J. Perkins, Andrew G. Barto: Lyapunov-Constrained Action Sets for Reinforcement Learning. ICML 2001: 409-416
30 Theodore J. Perkins, Andrew G. Barto: Heuristic Search in Infinite State Spaces Guided by Lyapunov Analysis. IJCAI 2001: 242-247
29 Michael T. Rosenstein, Andrew G. Barto: Robot Weightlifting By Direct Policy Search. IJCAI 2001: 839-846
28EEMichael Kositsky, Andrew G. Barto: The Emergence of Multiple Movement Units in the Presence of Noise and Feedback Delay. NIPS 2001: 43-50
27 Robert Moll, Theodore J. Perkins, Andrew G. Barto: Machine Learning for Subproblem Selection. ICML 2000: 615-622
26 Jette Randløv, Andrew G. Barto, Michael T. Rosenstein: Combining Reinforcement Learning with a Local Control Algorithm. ICML 2000: 775-782
25 Anders Jonsson, Andrew G. Barto: Automated State Abstraction for Options using the U-Tree Algorithm. NIPS 2000: 1054-1060
24 Andrew G. Barto, Andrew H. Fagg, Nathan Sitkoff, James C. Houk: A Cerebellar Model of Timing and Prediction in the Control of Reaching. Neural Computation 11(3): 565-594 (1999)
23EERobert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton: Learning Instance-Independent Value Functions to Enhance Local Search. NIPS 1998: 1017-1023
22EERichard S. Sutton, Andrew G. Barto: Reinforcement Learning: An Introduction. IEEE Transactions on Neural Networks 9(5): 1054-1054 (1998)
21 Robert H. Crites, Andrew G. Barto: Elevator Group Control Using Multiple Reinforcement Learning Agents. Machine Learning 33(2-3): 235-262 (1998)
20 Jeffrey F. Monaco, David G. Ward, Andrew G. Barto: Automated Aircraft Recovery via Reinforcement Learning: Initial Experiments. NIPS 1997
19EEMichael O. Duff, Andrew G. Barto: Local Bandit Approximation for Optimal Learning Problems. NIPS 1996: 1019-1025
18EEEric A. Hansen, Andrew G. Barto, Shlomo Zilberstein: Reinforcement Learning for Mixed Open-loop and Closed-loop Control. NIPS 1996: 1026-1032
17EERon Papka, James P. Callan, Andrew G. Barto: Text-Based Information Retrieval Using Exponentiated Gradient Descent. NIPS 1996: 3-9
16 Steven J. Bradtke, Andrew G. Barto: Linear Least-Squares Algorithms for Temporal Difference Learning. Machine Learning 22(1-3): 33-57 (1996)
15EERobert H. Crites, Andrew G. Barto: Improving Elevator Performance Using Reinforcement Learning. NIPS 1995: 1017-1023
14EEAndrew G. Barto, James C. Houk: A Predictive Switching Model of Cerebellar Movement Control. NIPS 1995: 138-144
13EEAndrew G. Barto, Steven J. Bradtke, Satinder P. Singh: Learning to Act Using Real-Time Dynamic Programming. Artif. Intell. 72(1-2): 81-138 (1995)
12 Vijaykumar Gullapalli, Andrew G. Barto, Roderic A. Grupen: Learning Admittance Mappings for Force-Guided Assembly. ICRA 1994: 2633-2638
11EERobert H. Crites, Andrew G. Barto: An Actor/Critic Algorithm that is Equivalent to Q-Learning. NIPS 1994: 401-408
10 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
9EESatinder P. Singh, Andrew G. Barto, Roderic A. Grupen, Christopher I. Connolly: Robust Reinforcement Learning in Motion Planning. NIPS 1993: 655-662
8EEAndrew G. Barto, Michael O. Duff: Monte Carlo Matrix Inversion and Reinforcement Learning. NIPS 1993: 687-694
7EEVijaykumar Gullapalli, Andrew G. Barto: Convergence of Indirect Adaptive Asynchronous Value Iteration Algorithms. NIPS 1993: 695-702
6EEN. 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
5 Robert A. Jacobs, Michael I. Jordan, Andrew G. Barto: Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. Cognitive Science 15(2): 219-250 (1991)
4 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
3EEAndrew G. Barto, Richard S. Sutton, Christopher J. C. H. Watkins: Sequential Decision Probelms and Neural Networks. NIPS 1989: 686-693
2 Oliver G. Selfridge, Richard S. Sutton, Andrew G. Barto: Training and Tracking in Robotics. IJCAI 1985: 670-672
1 Andrew G. Barto: A Note on Pattern Reproduction in Tessellation Structures. J. Comput. Syst. Sci. 16(3): 445-455 (1978)

Coauthor Index

1Ivon Arroyo [50] [59]
2N. E. Berthier [6]
3Steven J. Bradtke [13] [16]
4James P. Callan (Jamie Callan) [17]
5Nuttapong Chentanez [42]
6Christopher I. Connolly [9]
7Robert H. Crites [11] [15] [21]
8Toby Dragon [59]
9Michael O. Duff [8] [19]
10Richard E. A. Van Emmerik [47]
11Andrew H. Fagg [24]
12Kimberly Ferguson [50] [59]
13Don Fisher [59]
14Deepak Ganesan [55]
15Roderic A. Grupen [9] [12]
16Vijaykumar Gullapalli [7] [12]
17Eric A. Hansen [18]
18James C. Houk [6] [14] [24]
19Robert A. Jacobs [5] [10]
20Jeffrey Johns [59]
21Anders Jonsson [25] [46] [48] [56]
22Michael I. Jordan [5] [10]
23George Konidaris [49] [52] [57]
24Michael Kositsky [28] [33]
25Sridhar Mahadevan [38] [39] [50] [59]
26Vimal Mathew [58]
27Amy McGovern [32] [34]
28Hasmik Meheranian [59]
29Robert Moll (Robert N. Moll) [23] [27]
30Jeffrey F. Monaco [20]
31J. Eliot B. Moss [34]
32Ron Papka [17]
33Theodore J. Perkins [23] [27] [30] [31] [35]
34Marc Pickett [37]
35Jette Randløv [26]
36Balaraman Ravindran [36] [40] [41] [58]
37Michael T. Rosenstein [26] [29] [47]
38Sharad Saxena [4]
39Oliver G. Selfridge [2]
40Özgür Simsek [43] [44] [45] [51] [60]
41Satinder P. Singh [6] [9] [13] [42]
42Nathan Sitkoff [24]
43Richard S. Sutton [2] [3] [22] [23]
44Paul E. Utgoff [4]
45Christopher M. Vigorito [55]
46David G. Ward [20]
47Christopher J. C. H. Watkins [3]
48Alicia P. Wolfe [45] [53]
49Beverly Park Woolf [50] [59]
50Richard C. Yee [4]
51Shlomo Zilberstein [18]

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Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)