RL3, Rutgers Laboratory for Real-Life Reinforcement Learning

[ 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | ]

2009

Carlos Diuk, Lihong Li and Bethany R. Leffler, The Adaptive k-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning , Proceedings of the Twenty-Sixth International Conference on Machine Learning (ICML-09) , 2009.
Alexander L. Strehl and Lihong Li and Michael L. Littman, Reinforcement learning in general MDPs: PAC analysis , Journal of Machine Learning Research , Pages 2413--2444 , 2009.
Emma Brunskill and Bethany R. Leffler and Lihong Li and Michael L. Littman and Nichlos Roy, Provably efficient learning with typed parametric models , Journal of Machine Learning Research , Pages 1955--1988, , 2009.
Thomas J. Walsh and Ali Nouri and Lihong Li and Michael L. Littman, Planning and Learning in Environments with Delayed Feedback , Journal of Autonomous Agents and Multi-Agent Systems , Pages 83--105 , 2009.
Lihong Li and Michael L. Littman and Christopher R. Mansley, Online Exploration in Least-Squares Policy Iteration , Proceedings of the Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-09) , 2009.
Ali Nouri and Michael L. Littman, Multi-resolution Exploration in Continuous Spaces , Proceedings of Neural Information Processing Systems , 2009.
Thomas J. Walsh and István Szita and Carlos Diuk and Michael L. Littman, Exploring Compact Reinforcement-Learning Representations with Linear Regression , Proceedings of The 25th Conference on Uncertainty in Artificial Intelligence (UAI-09) , 2009.
John Asmuth and Lihong Li and Michael L. Littman and Ali Nouri and David Wingate, A Bayesian Sampling Approach to Exploration in Reinforcement Learning , Proceedings of The 25th Conference on Uncertainty in Artificial Intelligence (UAI-09) , 2009.

2008

Monica Babes and Enrique Munoz de Cote and Michael Littman, Social Reward Shaping in the Prisoner's Dilemma , Autonomous Agents and Multiagent Systems 2008 , 2008.
John Asmuth and Michael L. Littman and Robert Zinkov, Potential-based Shaping in Model-based Reinforcement Learning , Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08) , 2008.
Alexander L. Strehl and Michael L. Littman, Online Linear Regression and Its Application to Model-Based Reinforcement Learning , Proceedings of Neural Information Processing Systems , 2008.
Lihong Li and Michael L. Littman and Thomas J. Walsh, Knows What It Knows: A Framework for Self-Aware Learning , Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML-08) , 2008.
Lihong Li and Michael Littman, Efficient Value-Function Approximation via Online Linear Regression , International Symposium on Artificial Intelligence and Mathematics , 2008.
Bethany R. Leffler and Christopher R. Mansley and Michael L. Littman, Efficient Learning of Dynamics Models using Terrain Classification , Proceedings of the International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems , 2008.
Fusun Yaman and Thomas J. Walsh and Michael L. Littman and Marie desJardins, Democratic Approximation of Lexicographic Preference Models , Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML-08) , 2008.
Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, and Nicholas Roy, CORL: A Continuous-State Offset-Dynamics Reinforcement Learner , Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI-08) , 2008.
Carlos Diuk and Andre Cohen and Michael Littman, An Object-Oriented Representation for Efficient Reinforcement Learning , Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML-08) , 2008.
Ronald Parr and Lihong Li and Gavin Taylor and Christopher Painter-Wakefield and Michael L. Littman, An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning , Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML-08) , 2008.
Lihong Li, A Worst-Case Comparison between Temporal Difference and Residual Gradient with Linear Function Approximation , Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML-08) , 2008.
Thomas J. Walsh and Michael L. Littman, Efficient Learning of Action Schemas and Web-Service Descriptions , Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08) , 2008.

2007

Thomas J. Walsh and Michael L. Littman, Planning with Conceptual Models Mined from User Behavior , Proceedings of the AAAI-07 Workshop on Acquiring Planning Knowledge via Demonstration , 2007.
Thomas J. Walsh and Ali Nouri and Lihong Li and Michael L. Littman, Planning and Learning in Environments with Delayed Feedback , Proceedings of the 18th European Conference on Machine Learning (ECML-07) , 2007.
Fancong Zeng and Michael L. Littman, Just-in-time Failure Detection , ICAC-2007 Workshop on Adaptive Methods in Autonomic Computing Systems (AMACS) , 2007.
Amy Greenwald and Michael L. Littman, Introduction to the special issue on learning and computational game theory , Machine Learning , 2007.
Alexander L. Strehl and Carlos Diuk and Michael L. Littman, Efficient Structure Learning in Factored-state MDPs , Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI-07) , 2007.
Bethany R. Leffler and Michael L. Littman and Timothy Edmunds, Efficient Reinforcement Learning with Relocatable Action Models , Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI-07) , 2007.
Ronald Parr and Christopher Painter-Wakefield and Lihong Li and Michael Littman, Analyzing Feature Generation for Value-Function Approximation , International Conference on Machine Learning (ICML-2007) , 2007.
Martin Zinkevich and Amy Greenwald and Michael L. Littman, A hierarchy of prescriptive goals for multiagent learning , Artificial Intelligence , Pages 440--447 , 2007.
Thomas J. Walsh and Michael L. Littman, A Multiple Representation Approach To Learning Dynamical Systems , Computational Approaches to Representation Change During Learning and Development: AAAI Fall Symposium , 2007.

2006

Lihong Li and Thomas J. Walsh and Michael L. Littman, Towards a Unified Theory of State Abstraction for MDPs , Ninth International Symposium on Artificial Intelligence and Mathematics , 2006.
David L. Roberts and Mark J. Nelson and Isbell, Jr., Charles Lee and Michael Mateas and Michael L. Littman, Targeting Specific Distributions of Trajectories in MDPs , Proceedings of The Twenty-First National Conference on Artificial Intelligence , 2006.
Alexander L. Strehl and Lihong Li and Michael L. Littman, PAC Reinforcement Learning Bounds for RTDP and Rand-RTDP , AAAI 2006 Workshop on Learning For Search , 2006.
Alexander L. Strehl and Lihong Li and Eric Wiewiora and John Langford and Michael L. Littman, PAC Model-free Reinforcement Learning , Proceedings of the Twenty-third International Conference on Machine Learning (ICML-06) , 2006.
Alexander L. Strehl and Lihong Li and Michael L. Littman, Incremental Model-based Learners With Formal Learning-Time Guarantees , Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI 2006) , 2006.
Alexander L. Strehl and Chris Mesterharm and Michael L. Littman and Haym Hirsh, Experience-Efficient Learning in Associative Bandit Problems , Proceedings of the Twenty-third International Conference on Machine Learning (ICML-06) , 2006.
Michael L. Littman and Nishkam Ravi and Arjun Talwar and Martin Zinkevich, An Efficient Optimal-Equilibrium Algorithm for Two-Player Game Trees , Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI-06) , 2006.
Carlos Diuk and Michael Littman and Alexander Strehl, A Hierarchical Approach to Efficient Reinforcement Learning in Deterministic Domains , Fifth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-06) , 2006.

2005

Hakan L. S. Younes and Michael L. Littman and David Weissman and John Asmuth, The First Probabilistic Track of the International Planning Competition , Journal of Artificial Intelligence Research 24, Pages 851-887 , 2005.
Lihong Li and Michael L. Littman, Lazy Approximation for Solving Continuous Finite-Horizon MDPs , The Twentieth National Conference on Artificial Intelligence , Pages 1175--1180 , 2005.
Bethany R. Leffler and Michael L. Littman and Alexander L. Strehl and Thomas J. Walsh, Efficient Exploration With Latent Structure , Proceedings of Robotics: Science and Systems , 2005.
Martin Zinkevich and Amy R. Greenwald and Michael L. Littman, Cyclic Equilibria in Markov Games , Advances in Neural Information Processing Systems 18 , 2005.
Nishkam Ravi and Nikhil Dandekar and Preetham Mysore and Michael L. Littman, Activity Recognition from Accelerometer Data , Seventeenth Innovative Applications of Artificial Intelligence Conference , Pages 1541--1546 , 2005.
Alexander L. Strehl and Michael L. Littman, A Theoretical Analysis of Model-Based Interval Estimation , Proceedings of the Twenty-second International Conference on Machine Learning (ICML-05) , Pages 857--864 , 2005.

2004

David LeRoux and Michael Littman, Reinforcement Learning using LCS in Continuous State Space , 2004.
Michael L. Littman and Nishkam Ravi and Eitan Fenson and Rich Howard, Reinforcement Learning for Autonomic Network Repair , 1st International Conference on Autonomic Computing (ICAC 2004) , Pages 284--285 , 2004.
Alexander L. Strehl and Michael L. Littman, An Empirical Evaluation of Interval Estimation for Markov Decision Processes , The 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2004) , Pages 128--135 , 2004.

2003

Michael L. Littman and Thu Nguyen and Haym Hirsh and Eitan M. Fenson and Richard Howard, Cost-Sensitive Fault Remediation for Autonomic Computing , Workshop on AI and Autonomic Computing: Developing a Research Agenda for Self-Managing Computer Systems , 2003.