Exploration in relational domains for model-based reinforcement learning
暂无分享,去创建一个
[1] S. Krishnan,et al. A Probabilistic Training Scheme for the Time-Concentration Network , 1989, KBCS.
[2] Jürgen Schmidhuber,et al. Curious model-building control systems , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.
[3] Sebastian Thrun,et al. The role of exploration in learning control , 1992 .
[4] D. Sofge. THE ROLE OF EXPLORATION IN LEARNING CONTROL , 1992 .
[5] David A. Cohn,et al. Active Learning with Statistical Models , 1996, NIPS.
[6] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[7] Stephen Muggleton,et al. Learning from Positive Data , 1996, Inductive Logic Programming Workshop.
[8] Shan-Hwei Nienhuys-Cheng,et al. Foundations of Inductive Logic Programming , 1997, Lecture Notes in Computer Science.
[9] Stephen Muggleton. Inductive Logic Programming: 6th International Workshop, ILP-96, Stockholm, Sweden, August 26-28, 1996, Selected Papers , 1997 .
[10] Michael Kearns,et al. Efficient Reinforcement Learning in Factored MDPs , 1999, IJCAI.
[11] Hendrik Blockeel,et al. Top-Down Induction of First Order Logical Decision Trees , 1998, AI Commun..
[12] Craig Boutilier,et al. Symbolic Dynamic Programming for First-Order MDPs , 2001, IJCAI.
[13] Jan Ramon. Thesis: clustering and instance based learning in first order logic , 2002 .
[14] Jan Ramon,et al. Clustering and instance based learning in first order logic , 2002, AI Communications.
[15] Dale Schuurmans,et al. Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs , 2002, ICML.
[16] Ronen I. Brafman,et al. R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning , 2001, J. Mach. Learn. Res..
[17] Carlos Guestrin,et al. Generalizing plans to new environments in relational MDPs , 2003, IJCAI 2003.
[18] Sham M. Kakade,et al. On the sample complexity of reinforcement learning. , 2003 .
[19] John Langford,et al. Exploration in Metric State Spaces , 2003, ICML.
[20] Luc De Raedt,et al. Bellman goes relational , 2004, ICML.
[21] Saso Dzeroski,et al. Integrating Guidance into Relational Reinforcement Learning , 2004, Machine Learning.
[22] Michael Kearns,et al. Near-Optimal Reinforcement Learning in Polynomial Time , 2002, Machine Learning.
[23] S. Sanner. Simultaneous Learning of Structure and Value in Relational Reinforcement Learning , 2005 .
[24] Steffen Hölldobler,et al. FluCaP: A Heuristic Search Planner for First-Order MDPs , 2006, J. Artif. Intell. Res..
[25] Thomas Gärtner,et al. Graph kernels and Gaussian processes for relational reinforcement learning , 2006, Machine Learning.
[26] Jesse Hoey,et al. An analytic solution to discrete Bayesian reinforcement learning , 2006, ICML.
[27] Scott Sanner,et al. Online Feature Discovery in Relational Reinforcement Learning , 2006 .
[28] Roni Khardon,et al. First Order Decision Diagrams for Relational MDPs , 2007, IJCAI.
[29] Maurice Bruynooghe,et al. Online Learning and Exploiting Relational Models in Reinforcement Learning , 2007, IJCAI.
[30] Jennifer Neville,et al. Relational Dependency Networks , 2007, J. Mach. Learn. Res..
[31] Kurt Driessens,et al. Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling , 2007, ECML.
[32] Andrew McCallum,et al. Introduction to Statistical Relational Learning , 2007 .
[33] Michael L. Littman,et al. Online Linear Regression and Its Application to Model-Based Reinforcement Learning , 2007, NIPS.
[34] Peter Geibel,et al. Learning Models of Relational MDPs Using Graph Kernels , 2007, MICAI.
[35] Ben Taskar,et al. Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) , 2007 .
[36] L. P. Kaelbling,et al. Learning Symbolic Models of Stochastic Domains , 2007, J. Artif. Intell. Res..
[37] Luc De Raedt,et al. Probabilistic Inductive Logic Programming , 2004, Probabilistic Inductive Logic Programming.
[38] Thomas J. Walsh,et al. Knows what it knows: a framework for self-aware learning , 2008, ICML '08.
[39] Andre Cohen,et al. An object-oriented representation for efficient reinforcement learning , 2008, ICML '08.
[40] Gerald DeJong,et al. Active reinforcement learning , 2008, ICML '08.
[41] Kristian Kersting,et al. Non-parametric policy gradients: a unified treatment of propositional and relational domains , 2008, ICML '08.
[42] Thomas J. Walsh,et al. Efficient Learning of Action Schemas and Web-Service Descriptions , 2008, AAAI.
[43] Andrew Y. Ng,et al. Near-Bayesian exploration in polynomial time , 2009, ICML '09.
[44] Lihong Li,et al. Reinforcement Learning in Finite MDPs: PAC Analysis , 2009, J. Mach. Learn. Res..
[45] Lihong Li,et al. The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning , 2009, ICML '09.
[46] Thomas J. Walsh,et al. Exploring compact reinforcement-learning representations with linear regression , 2009, UAI.
[47] Michael L. Littman,et al. A unifying framework for computational reinforcement learning theory , 2009 .
[48] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[49] Scott Sanner,et al. Practical solution techniques for first-order MDPs , 2009, Artif. Intell..
[50] Marc Toussaint,et al. Relevance Grounding for Planning in Relational Domains , 2009, ECML/PKDD.
[51] Michael L. Littman,et al. Dimension reduction and its application to model-based exploration in continuous spaces , 2010, Machine Learning.
[52] Kristian Kersting,et al. Self-Taught Decision Theoretic Planning with First Order Decision Diagrams , 2010, ICAPS.
[53] Marc Toussaint,et al. Integrated motor control, planning, grasping and high-level reasoning in a blocks world using probabilistic inference , 2010, 2010 IEEE International Conference on Robotics and Automation.
[54] Lise Getoor,et al. Active Learning for Networked Data , 2010, ICML.
[55] Kristian Kersting,et al. Boosting relational dependency networks , 2010, ILP - 2010.
[56] Thomas J. Walsh,et al. Efficient learning of relational models for sequential decision making , 2010 .
[57] Thorsten Joachims,et al. Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes , 2010, ECML/PKDD.
[58] David Windridge,et al. Perception-action learning as an epistemologically-consistent model for self-updating cognitive representation. , 2010, Advances in experimental medicine and biology.
[59] Marc Toussaint,et al. Planning with Noisy Probabilistic Relational Rules , 2010, J. Artif. Intell. Res..
[60] Tobias Lang,et al. Planning and exploration in stochastic relational worlds , 2011 .
[61] De,et al. Relational Reinforcement Learning , 2022 .