Littman, Michael L.

101 publications

NeurIPS 2024 Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris

NeurIPS 2022 Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex Charles Lovering, Jessica Forde, George Konidaris, Ellie Pavlick, Michael L. Littman

NeurIPS 2022 Faster Deep Reinforcement Learning with Slower Online Network Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alexander J Smola

NeurIPS 2022 Model-Based Lifelong Reinforcement Learning with Bayesian Exploration Haotian Fu, Shangqun Yu, Michael L. Littman, George Konidaris

IJCAI 2022 On the (In)Tractability of Reinforcement Learning for LTL Objectives Cambridge Yang, Michael L. Littman, Michael Carbin

IJCAI 2022 On the Expressivity of Markov Reward (Extended Abstract) David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh

AAAI 2021 Deep Radial-Basis Value Functions for Continuous Control Kavosh Asadi, Neev Parikh, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman

AAAI 2021 Lipschitz Lifelong Reinforcement Learning Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman

NeurIPS 2021 On the Expressivity of Markov Reward David Abel, Will Dabney, Anna Harutyunyan, Mark K Ho, Michael L. Littman, Doina Precup, Satinder P. Singh

AAAI 2021 Towards Sample Efficient Agents Through Algorithmic Alignment (Student Abstract) Mingxuan Li, Michael L. Littman

AAAI 2020 People Do Not Just Plan, They Plan to Plan Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths

JMLR 2020 Successor Features Combine Elements of Model-Free and Model-Based Reinforcement Learning Lucas Lehnert, Michael L. Littman

IJCAI 2019 DeepMellow: Removing the Need for a Target Network in Deep Q-Learning Seungchan Kim, Kavosh Asadi, Michael L. Littman, George Dimitri Konidaris

AAAI 2019 State Abstraction as Compression in Apprenticeship Learning David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong

IJCAI 2019 The Expected-Length Model of Options David Abel, John Winder, Marie desJardins, Michael L. Littman

AAAI 2019 Theory of Minds: Understanding Behavior in Groups Through Inverse Planning Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum

AAAI 2018 Bandit-Based Solar Panel Control David Abel, Edward C. Williams, Stephen Brawner, Emily Reif, Michael L. Littman

ICML 2017 An Alternative SoftMax Operator for Reinforcement Learning Kavosh Asadi, Michael L. Littman

ICML 2017 Interactive Learning from Policy-Dependent Human Feedback James MacGlashan, Mark K. Ho, Robert Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman

IJCAI 2015 Between Imitation and Intention Learning James MacGlashan, Michael L. Littman

AAAI 2014 A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, Jeff Huang, David L. Roberts

AAAI 2013 AAAI-13 Preface Marie desJardins, Michael L. Littman

AAAI 2013 Open-Loop Planning in Large-Scale Stochastic Domains Ari Weinstein, Michael L. Littman

AAAI 2013 Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 14-18, 2013, Bellevue, Washington, USA Marie desJardins, Michael L. Littman

AAAI 2012 Covering Number as a Complexity Measure for POMDP Planning and Learning Zongzhang Zhang, Michael L. Littman, Xiaoping Chen

ICML 2011 Apprenticeship Learning About Multiple Intentions Monica Babes, Vukosi Marivate, Kaushik Subramanian, Michael L. Littman

MLJ 2011 Introduction to the Special Issue on Empirical Evaluations in Reinforcement Learning Shimon Whiteson, Michael L. Littman

MLJ 2011 Knows What It Knows: A Framework for Self-Aware Learning Lihong Li, Michael L. Littman, Thomas J. Walsh, Alexander L. Strehl

UAI 2011 Learning Is Planning: Near Bayes-Optimal Reinforcement Learning via Monte-Carlo Tree Search John Asmuth, Michael L. Littman

ICML 2010 Classes of Multiagent Q-Learning Dynamics with Epsilon-Greedy Exploration Michael Wunder, Michael L. Littman, Monica Babes

MLJ 2010 Dimension Reduction and Its Application to Model-Based Exploration in Continuous Spaces Ali Nouri, Michael L. Littman

ICML 2010 Generalizing Apprenticeship Learning Across Hypothesis Classes Thomas J. Walsh, Kaushik Subramanian, Michael L. Littman, Carlos Diuk

AAAI 2010 Integrating Sample-Based Planning and Model-Based Reinforcement Learning Thomas J. Walsh, Sergiu Goschin, Michael L. Littman

UAI 2009 A Bayesian Sampling Approach to Exploration in Reinforcement Learning John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate

UAI 2009 Exploring Compact Reinforcement-Learning Representations with Linear Regression Thomas J. Walsh, Istvan Szita, Carlos Diuk, Michael L. Littman

ICML 2009 Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009 Andrea Pohoreckyj Danyluk, Léon Bottou, Michael L. Littman

JMLR 2009 Provably Efficient Learning with Typed Parametric Models Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy

JMLR 2009 Reinforcement Learning in Finite MDPs: PAC Analysis Alexander L. Strehl, Lihong Li, Michael L. Littman

UAI 2008 A Polynomial-Time Nash Equilibrium Algorithm for Repeated Stochastic Games Enrique Munoz de Cote, Michael L. Littman

ICML 2008 An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman

ICML 2008 An Object-Oriented Representation for Efficient Reinforcement Learning Carlos Diuk, Andre Cohen, Michael L. Littman

UAI 2008 CORL: A Continuous-State Offset-Dynamics Reinforcement Learner Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy

ICML 2008 Democratic Approximation of Lexicographic Preference Models Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins

AAAI 2008 Efficient Learning of Action Schemas and Web-Service Descriptions Thomas J. Walsh, Michael L. Littman

ICML 2008 Knows What It Knows: A Framework for Self-Aware Learning Lihong Li, Michael L. Littman, Thomas J. Walsh

NeurIPS 2008 Multi-Resolution Exploration in Continuous Spaces Ali Nouri, Michael L. Littman

AAAI 2008 Potential-Based Shaping in Model-Based Reinforcement Learning John Asmuth, Michael L. Littman, Robert Zinkov

ICML 2007 Analyzing Feature Generation for Value-Function Approximation Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman

AAAI 2007 Efficient Reinforcement Learning with Relocatable Action Models Bethany R. Leffler, Michael L. Littman, Timothy Edmunds

AAAI 2007 Efficient Structure Learning in Factored-State MDPs Alexander L. Strehl, Carlos Diuk, Michael L. Littman

MLJ 2007 Introduction to the Special Issue on Learning and Computational Game Theory Amy Greenwald, Michael L. Littman

NeurIPS 2007 Online Linear Regression and Its Application to Model-Based Reinforcement Learning Alexander L. Strehl, Michael L. Littman

UAI 2006 An Efficient Optimal-Equilibrium Algorithm for Two-Player Game Trees Michael L. Littman, Nishkam Ravi, Arjun Talwar, Martin Zinkevich

ICML 2006 Experience-Efficient Learning in Associative Bandit Problems Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh

UAI 2006 Incremental Model-Based Learners with Formal Learning-Time Guarantees Alexander L. Strehl, Lihong Li, Michael L. Littman

ICML 2006 PAC Model-Free Reinforcement Learning Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman

AAAI 2006 Targeting Specific Distributions of Trajectories in MDPs David L. Roberts, Mark J. Nelson, Charles Lee Isbell Jr., Michael Mateas, Michael L. Littman

ICML 2005 A Theoretical Analysis of Model-Based Interval Estimation Alexander L. Strehl, Michael L. Littman

AAAI 2005 Activity Recognition from Accelerometer Data Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, Michael L. Littman

MLJ 2005 Corpus-Based Learning of Analogies and Semantic Relations Peter D. Turney, Michael L. Littman

NeurIPS 2005 Cyclic Equilibria in Markov Games Martin Zinkevich, Amy Greenwald, Michael L. Littman

AAAI 2005 Lazy Approximation for Solving Continuous Finite-Horizon MDPs Lihong Li, Michael L. Littman

JAIR 2005 The First Probabilistic Track of the International Planning Competition Håkan L. S. Younes, Michael L. Littman, David Weissman, John Asmuth

AAAI 2004 An Instance-Based State Representation for Network Repair Michael L. Littman, Nishkam Ravi, Eitan Fenson, Richard E. Howard

JAIR 2003 Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik, David A. McAllester

ICML 2003 Learning Predictive State Representations Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone

COLT 2003 Tutorial: Learning Topics in Game-Theoretic Decision Making Michael L. Littman

ICML 2002 Modeling Auction Price Uncertainty Using Boosting-Based Conditional Density Estimation Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik

JAIR 2001 ATTac-2000: An Adaptive Autonomous Bidding Agent Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns

NeurIPS 2001 An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games Michael L. Littman, Michael J. Kearns, Satinder P. Singh

ICML 2001 Friend-or-Foe Q-Learning in General-Sum Games Michael L. Littman

UAI 2001 Graphical Models for Game Theory Michael J. Kearns, Michael L. Littman, Satinder Singh

NeurIPS 2001 PAC Generalization Bounds for Co-Training Sanjoy Dasgupta, Michael L. Littman, David A. McAllester

NeurIPS 2001 Predictive Representations of State Michael L. Littman, Richard S. Sutton

ICML 2000 Algorithm Selection Using Reinforcement Learning Michail G. Lagoudakis, Michael L. Littman

ICML 2000 Approximate Dimension Equalization in Vector-Based Information Retrieval Fan Jiang, Michael L. Littman

MLJ 2000 Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms Satinder Singh, Tommi S. Jaakkola, Michael L. Littman, Csaba Szepesvári

NeurIPS 2000 Exact Solutions to Time-Dependent MDPs Justin A. Boyan, Michael L. Littman

AAAI 2000 Reinforcement Learning for Algorithm Selection Michail G. Lagoudakis, Michael L. Littman

AAAI 2000 Towards Approximately Optimal Poker Jiefu Shi, Michael L. Littman

NeCo 1999 A Unified Analysis of Value-Function-Based Reinforcement Learning Algorithms Csaba Szepesvári, Michael L. Littman

AAAI 1999 Contingent Planning Under Uncertainty via Stochastic Satisfiability Stephen M. Majercik, Michael L. Littman

AAAI 1999 Initial Experiments in Stochastic Satisfiability Michael L. Littman

AAAI 1999 PROVERB: The Probabilistic Cruciverbalist Greg A. Keim, Noam M. Shazeer, Michael L. Littman, Sushant Agarwal, Catherine M. Cheves, Joseph Fitzgerald, Jason Grosland, Fan Jiang, Shannon Pollard, Karl Weinmeister

AAAI 1999 Solving Crossword Puzzles as Probabilistic Constraint Satisfaction Noam M. Shazeer, Michael L. Littman, Greg A. Keim

AAAI 1999 Solving Crosswords with PROVERB Michael L. Littman, Greg A. Keim, Noam M. Shazeer

ICML 1998 Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus Michael L. Littman, Fan Jiang, Greg A. Keim

JAIR 1998 The Computational Complexity of Probabilistic Planning Michael L. Littman, Judy Goldsmith, Martin Mundhenk

AAAI 1998 Using Caching to Solve Larger Probabilistic Planning Problems Stephen M. Majercik, Michael L. Littman

UAI 1997 Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang

AAAI 1997 Probabilistic Propositional Planning: Representations and Complexity Michael L. Littman

AAAI 1997 Speeding Safely: Multi-Criteria Optimization in Probabilistic Planning Michael S. Fulkerson, Michael L. Littman, Greg A. Keim

UAI 1997 The Complexity of Plan Existence and Evaluation in Probabilistic Domains Judy Goldsmith, Michael L. Littman, Martin Mundhenk

ICML 1996 A Generalized Reinforcement-Learning Model: Convergence and Applications Michael L. Littman, Csaba Szepesvári

JAIR 1996 Reinforcement Learning: A Survey Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore

ICML 1995 Learning Policies for Partially Observable Environments: Scaling up Michael L. Littman, Anthony R. Cassandra, Leslie Pack Kaelbling

UAI 1995 On the Complexity of Solving Markov Decision Problems Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling

AAAI 1994 Acting Optimally in Partially Observable Stochastic Domains Anthony R. Cassandra, Leslie Pack Kaelbling, Michael L. Littman

ICML 1994 Markov Games as a Framework for Multi-Agent Reinforcement Learning Michael L. Littman

NeurIPS 1993 Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach Justin A. Boyan, Michael L. Littman

NeurIPS 1989 Generalization and Scaling in Reinforcement Learning David H. Ackley, Michael L. Littman