Temporal Abstraction in Temporal-difference Networks
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[1] Ronald L. Rivest,et al. Diversity-based inference of finite automata , 1994, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).
[2] Gary L. Drescher,et al. Made-up minds - a constructivist approach to artificial intelligence , 1991 .
[3] Richard S. Sutton,et al. Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta , 1992, AAAI.
[4] Gerald Tesauro,et al. Temporal Difference Learning and TD-Gammon , 1995, J. Int. Comput. Games Assoc..
[5] Mark B. Ring. Continual learning in reinforcement environments , 1995, GMD-Bericht.
[6] Richard S. Sutton,et al. TD Models: Modeling the World at a Mixture of Time Scales , 1995, ICML.
[7] Ben J. A. Kröse,et al. Learning from delayed rewards , 1995, Robotics Auton. Syst..
[8] Andrew G. Barto,et al. Improving Elevator Performance Using Reinforcement Learning , 1995, NIPS.
[9] Leemon C. Baird,et al. Residual Algorithms: Reinforcement Learning with Function Approximation , 1995, ICML.
[10] Andrew McCallum,et al. Reinforcement learning with selective perception and hidden state , 1996 .
[11] John N. Tsitsiklis,et al. Neuro-Dynamic Programming , 1996, Encyclopedia of Machine Learning.
[12] Michael L. Littman,et al. Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes , 1997, UAI.
[13] H. Jaeger,et al. A short introduction to observable operator models of stochastic processes , 1998 .
[14] Thomas G. Dietterich. The MAXQ Method for Hierarchical Reinforcement Learning , 1998, ICML.
[15] Richard S. Sutton,et al. Introduction to Reinforcement Learning , 1998 .
[16] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[17] R. Sutton,et al. Off-policy Learning with Recognizers , 2000 .
[18] Herbert Jaeger,et al. Observable Operator Models for Discrete Stochastic Time Series , 2000, Neural Computation.
[19] Richard S. Sutton,et al. Predictive Representations of State , 2001, NIPS.
[20] Sanjoy Dasgupta,et al. Off-Policy Temporal Difference Learning with Function Approximation , 2001, ICML.
[21] Satinder P. Singh,et al. A Nonlinear Predictive State Representation , 2003, NIPS.
[22] Matthew W. Mitchell,et al. Using Markov-k Memory for Problems with Hidden-State , 2003, MLMTA.
[23] Peter Stone,et al. Learning Predictive State Representations , 2003, ICML.
[24] Charles Lee Isbell,et al. Schema Learning: Experience-Based Construction of Predictive Action Models , 2004, NIPS.
[25] Michael R. James,et al. Learning and discovery of predictive state representations in dynamical systems with reset , 2004, ICML.
[26] Michael R. James,et al. Predictive State Representations: A New Theory for Modeling Dynamical Systems , 2004, UAI.
[27] Michael L. Littman,et al. Planning with predictive state representations , 2004, 2004 International Conference on Machine Learning and Applications, 2004. Proceedings..
[28] Sebastian Thrun,et al. Learning low dimensional predictive representations , 2004, ICML.
[29] Richard S. Sutton,et al. Temporal-Difference Networks , 2004, NIPS.
[30] Michael R. James,et al. Planning in Models that Combine Memory with Predictive Representations of State , 2005, AAAI.
[31] Richard S. Sutton,et al. Temporal-Difference Networks with History , 2005, IJCAI.
[32] Richard S. Sutton,et al. TD(λ) networks: temporal-difference networks with eligibility traces , 2005, ICML.
[33] Eric Wiewiora,et al. Learning predictive representations from a history , 2005, ICML.
[34] Michael R. James,et al. Combining Memory and Landmarks with Predictive State Representations , 2005, IJCAI.
[35] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[36] Richard S. Sutton,et al. Using Predictive Representations to Improve Generalization in Reinforcement Learning , 2005, IJCAI.
[37] Richard S. Sutton,et al. Learning to predict by the methods of temporal differences , 1988, Machine Learning.
[38] Michael R. James,et al. Learning predictive state representations in dynamical systems without reset , 2005, ICML.
[39] Michael H. Bowling,et al. Online Discovery and Learning of Predictive State Representations , 2005, NIPS.
[40] Joelle Pineau,et al. Representing Systems with Hidden State , 2006, AAAI.
[41] Satinder P. Singh,et al. Predictive linear-Gaussian models of controlled stochastic dynamical systems , 2006, ICML.
[42] Satinder P. Singh,et al. Mixtures of Predictive Linear Gaussian Models for Nonlinear, Stochastic Dynamical Systems , 2006, AAAI.
[43] Satinder P. Singh,et al. Predictive state representations with options , 2006, ICML.
[44] Michael H. Bowling,et al. Learning predictive state representations using non-blind policies , 2006, ICML '06.
[45] Satinder P. Singh,et al. Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems , 2006, ICML.