Learning to predict by the methods of temporal differences

Keywords

Temporal difference learning, Computer science, Artificial intelligence, Machine learning, Heuristic

References

• A Unified Theory of Heuristic Evaluation Functions and its Application to LearningJens Christensen, Richard E. Korf — 1986
• Learning and problem-solving with multilayer connectionist systems (adaptive, strategy learning, neural networks, reinforcement learning)Charles William Anderson — 1986
• Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systemsJohn H. Holland — 1995
• A Computer and Communication Network Performance Analysis Primer (Prentice Hall, Englewood Cliffs, NJ, 1985; revised, 1987)Bart W. Stuck, E. Arthurs — 1987
• Intelligent Behavior as an Adaptation to the Task EnvironmentLashon B. Booker — 1982
• Learning by statistical cooperation of self-interested neuron-like computing elements.Andy Barto — 1985
• Temporal credit assignment in reinforcement learningRichard S. Sutton — 1984
• Dynamic Programming: Models and ApplicationsEric V. Denardo — 2003
• Learning to Predict SequencesTom Dietterich, Ryszard S. Michalski — 1985
• Neuronlike adaptive elements that can solve difficult learning control problemsAndrew G. Barto, Richard S. Sutton, Charles W. Anderson — 1983
• Computers and ThoughtEdward A. Feigenbaum, Julian Feldman — 1963
• Simulation of the classically conditioned nictitating membrane response by a neuron-like adaptive element: response topography, neuronal firing, and interstimulus intervals.John W. Moore, John E. Desmond, Neil E. Berthier — 1986
• Temporal primacy overrides prior training in serial compound conditioning of the rabbit’s nictitating membrane responseE. James Kehoe, Bernard G. Schreurs, Peita Graham — 1987
• Disjunctive models of Boolean category learningSteven E. Hampson, Dennis J. Volper — 1987
• Toward a modern theory of adaptive networks: expectation and prediction.Richard S. Sutton, Andrew G. Barto — 1981
• A Learning Algorithm for Boltzmann MachinesDavid H. Ackley, Geoffrey E. Hinton, Terrence J. Sejnowski — 1985
• An Adaptive Optimal Controller for Discrete-Time Markov EnvironmentsIan H. Witten — 1977
• Matrix Iterative AnalysisRichard S. Varga, J. Gillis — 1961
• Finite Markov chainsJohn G. Kemeny, J. Laurie Snell — 1960
• A neuronal model of classical conditioningA. Harry Klopf — 1988

Cited by

• Autonomous perceptual feature extraction in a topology-constrained architectureSylvain Chartier, Gyslain Giguère — 2008
• Biologically-Inspired Computing Approaches To Cognitive Systems: a partial tour of the literatureDave Cliff — 2003
• Epistemological Databases for Probabilistic Knowledge Base ConstructionMichael Wick — 2015
• Minimal Residual Approaches for Policy Evaluation in Large Sparse Markov ChainsHengshuai Yao, Zhiqiang Liu — 2008
• Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum.Michael W. Easson, Jeffery A. Mertens, Chanel Fortier — 2006
• Hierarchical reinforcement learning: a hybrid approachMalcolm Ryan — 2004
• Least Squares Solutions of the HJB Equation With Neural Network Value-Function ApproximatorsSandrine Torandell — 2007
• Chained learning architectures in a simple closed-loop behavioural contextTomas Kulvičius, Bernd Porr, Florentin Wörgötter — 2007
• Posterior Weighted Reinforcement Learning with State UncertaintyCarla Bozzolo, Ezio Ornato — 2010
• Learning where to look for a hidden targetJ.J.W. Coppen, G.A. Hone — 2013
• The Origins and Organization of Vertebrate Pavlovian Conditioning.Shelby Lawson — 2015
• Heuristic Selection of Actions in Multiagent Reinforcement LearningReinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna Helena Reali Costa — 2007
• Hybridisation of expertise and reinforcement learning in dialogue systemsRomain Laroche, Ghislain Putois, Philippe Bretier — 2009
• Stochastic Enforced Hill-ClimbingJia‐Hong Wu, Rajesh Kalyanam, Robert Givan — 2008
• Training a Back-Propagation Network with Temporal Difference Learning and a database for the board game PenteV.C. Muijrers — 2011
• Distributed Decision-Making and TaskCoordination in Dynamic, Uncertain andReal-Time Multiagent EnvironmentsSébastien Paquet — 2005
• Evolving A Sense Of ValencyTheodoros Damoulas — 2004
• Combining Entropy Based Heuristics and Min-Max Search with Temporal Differences to Play Hidden State GamesGregory Calbert, Hing-Wah Kwok — 2004
• Learning to Play Games from Multiple Imperfect TeachersJohn Karlsson — 2014
• A new Q(lambda) with interim forward view and Monte Carlo equivalenceRich Sutton, Ashique Rupam Mahmood, Doina Precup — 2014

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