2008 | ||
---|---|---|
60 | EE | Özgür Simsek, Andrew G. Barto: Skill Characterization Based on Betweenness. NIPS 2008: 1497-1504 |
2007 | ||
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 | |
58 | EE | Balaraman Ravindran, Andrew G. Barto, Vimal Mathew: Deictic Option Schemas. IJCAI 2007: 1023-1028 |
57 | EE | George Konidaris, Andrew G. Barto: Building Portable Options: Skill Transfer in Reinforcement Learning. IJCAI 2007: 895-900 |
56 | EE | Anders Jonsson, Andrew G. Barto: Active Learning of Dynamic Bayesian Networks in Markov Decision Processes. SARA 2007: 273-284 |
55 | EE | Christopher M. Vigorito, Deepak Ganesan, Andrew G. Barto: Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks. SECON 2007: 21-30 |
54 | EE | Andrew G. Barto: Temporal difference learning. Scholarpedia 2(11): 1604 (2007) |
2006 | ||
53 | Alicia P. Wolfe, Andrew G. Barto: Decision Tree Methods for Finding Reusable MDP Homomorphisms. AAAI 2006 | |
52 | EE | George Konidaris, Andrew G. Barto: Autonomous shaping: knowledge transfer in reinforcement learning. ICML 2006: 489-496 |
51 | EE | Özgür Simsek, Andrew G. Barto: An intrinsic reward mechanism for efficient exploration. ICML 2006: 833-840 |
50 | EE | 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 |
49 | EE | George Konidaris, Andrew G. Barto: An Adaptive Robot Motivational System. SAB 2006: 346-356 |
48 | EE | Anders Jonsson, Andrew G. Barto: Causal Graph Based Decomposition of Factored MDPs. Journal of Machine Learning Research 7: 2259-2301 (2006) |
47 | EE | Michael 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) |
2005 | ||
46 | EE | Anders Jonsson, Andrew G. Barto: A causal approach to hierarchical decomposition of factored MDPs. ICML 2005: 401-408 |
45 | EE | Özgür Simsek, Alicia P. Wolfe, Andrew G. Barto: Identifying useful subgoals in reinforcement learning by local graph partitioning. ICML 2005: 816-823 |
44 | EE | Özgür Simsek, Andrew G. Barto: Learning Skills in Reinforcement Learning Using Relative Novelty. SARA 2005: 367-374 |
2004 | ||
43 | EE | Özgür Simsek, Andrew G. Barto: Using relative novelty to identify useful temporal abstractions in reinforcement learning. ICML 2004 |
42 | EE | Satinder P. Singh, Andrew G. Barto, Nuttapong Chentanez: Intrinsically Motivated Reinforcement Learning. NIPS 2004 |
2003 | ||
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 | |
39 | EE | Andrew G. Barto, Sridhar Mahadevan: Recent Advances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems 13(1-2): 41-77 (2003) |
38 | EE | Andrew G. Barto, Sridhar Mahadevan: Recent Advances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems 13(4): 341-379 (2003) |
2002 | ||
37 | Marc Pickett, Andrew G. Barto: PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning. ICML 2002: 506-513 | |
36 | EE | Balaraman Ravindran, Andrew G. Barto: Model Minimization in Hierarchical Reinforcement Learning. SARA 2002: 196-211 |
35 | EE | Theodore 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) | |
33 | EE | 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) |
2001 | ||
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 | |
28 | EE | Michael Kositsky, Andrew G. Barto: The Emergence of Multiple Movement Units in the Presence of Noise and Feedback Delay. NIPS 2001: 43-50 |
2000 | ||
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 | |
1999 | ||
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) | |
1998 | ||
23 | EE | Robert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton: Learning Instance-Independent Value Functions to Enhance Local Search. NIPS 1998: 1017-1023 |
22 | EE | Richard 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) | |
1997 | ||
20 | Jeffrey F. Monaco, David G. Ward, Andrew G. Barto: Automated Aircraft Recovery via Reinforcement Learning: Initial Experiments. NIPS 1997 | |
1996 | ||
19 | EE | Michael O. Duff, Andrew G. Barto: Local Bandit Approximation for Optimal Learning Problems. NIPS 1996: 1019-1025 |
18 | EE | Eric A. Hansen, Andrew G. Barto, Shlomo Zilberstein: Reinforcement Learning for Mixed Open-loop and Closed-loop Control. NIPS 1996: 1026-1032 |
17 | EE | Ron 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) | |
1995 | ||
15 | EE | Robert H. Crites, Andrew G. Barto: Improving Elevator Performance Using Reinforcement Learning. NIPS 1995: 1017-1023 |
14 | EE | Andrew G. Barto, James C. Houk: A Predictive Switching Model of Cerebellar Movement Control. NIPS 1995: 138-144 |
13 | EE | 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) |
1994 | ||
12 | Vijaykumar Gullapalli, Andrew G. Barto, Roderic A. Grupen: Learning Admittance Mappings for Force-Guided Assembly. ICRA 1994: 2633-2638 | |
11 | EE | Robert H. Crites, Andrew G. Barto: An Actor/Critic Algorithm that is Equivalent to Q-Learning. NIPS 1994: 401-408 |
1993 | ||
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 | |
9 | EE | Satinder P. Singh, Andrew G. Barto, Roderic A. Grupen, Christopher I. Connolly: Robust Reinforcement Learning in Motion Planning. NIPS 1993: 655-662 |
8 | EE | Andrew G. Barto, Michael O. Duff: Monte Carlo Matrix Inversion and Reinforcement Learning. NIPS 1993: 687-694 |
7 | EE | Vijaykumar Gullapalli, Andrew G. Barto: Convergence of Indirect Adaptive Asynchronous Value Iteration Algorithms. NIPS 1993: 695-702 |
1991 | ||
6 | EE | 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 |
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) | |
1990 | ||
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 | |
1989 | ||
3 | EE | Andrew G. Barto, Richard S. Sutton, Christopher J. C. H. Watkins: Sequential Decision Probelms and Neural Networks. NIPS 1989: 686-693 |
1985 | ||
2 | Oliver G. Selfridge, Richard S. Sutton, Andrew G. Barto: Training and Tracking in Robotics. IJCAI 1985: 670-672 | |
1978 | ||
1 | Andrew G. Barto: A Note on Pattern Reproduction in Tessellation Structures. J. Comput. Syst. Sci. 16(3): 445-455 (1978) |