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Deep Q-Network with Proximal Iteration


Dec 10, 2021
Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Michael L. Littman, Alexander J. Smola

* Work in Progress 

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On the Expressivity of Markov Reward


Nov 01, 2021
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh

* Accepted to NeurIPS 2021 

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Bad-Policy Density: A Measure of Reinforcement Learning Hardness


Oct 07, 2021
David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong

* Presented at the 2021 ICML Workshop on Reinforcement Learning Theory 

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Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback


Sep 15, 2021
Ishaan Shah, David Halpern, Kavosh Asadi, Michael L. Littman

* Accepted into ICML 2021 workshops Human-AI Collaboration in Sequential Decision-Making and Human in the Loop Learning 

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Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program


Jun 10, 2021
Jeff Druce, James Niehaus, Vanessa Moody, David Jensen, Michael L. Littman


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Control of mental representations in human planning


May 14, 2021
Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths


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Towards Sample Efficient Agents through Algorithmic Alignment


Sep 08, 2020
Mingxuan Li, Michael L. Littman


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The Efficiency of Human Cognition Reflects Planned Information Processing


Feb 13, 2020
Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths

* 13 pg (incl. supplemental materials); included in Proceedings of the 34th AAAI Conference on Artificial Intelligence 

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Learning State Abstractions for Transfer in Continuous Control


Feb 08, 2020
Kavosh Asadi, David Abel, Michael L. Littman


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Deep RBF Value Functions for Continuous Control


Feb 05, 2020
Kavosh Asadi, Ronald E. Parr, George D. Konidaris, Michael L. Littman


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Lipschitz Lifelong Reinforcement Learning


Jan 17, 2020
Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman

* Submitted to ICML 2020, 21 pages, 15 figures 

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Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging


Dec 08, 2019
John R. Zech, Jessica Zosa Forde, Michael L. Littman

* J.Z. and J.F. contributed equally to this work 

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Stackelberg Punishment and Bully-Proofing Autonomous Vehicles


Aug 23, 2019
Matt Cooper, Jun Ki Lee, Jacob Beck, Joshua D. Fishman, Michael Gillett, Zoë Papakipos, Aaron Zhang, Jerome Ramos, Aansh Shah, Michael L. Littman

* 10 pages, The 11th International Conference on Social Robotics 

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Interactive Learning of Environment Dynamics for Sequential Tasks


Jul 19, 2019
Robert Loftin, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts


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Deep Reinforcement Learning from Policy-Dependent Human Feedback


Feb 12, 2019
Dilip Arumugam, Jun Ki Lee, Sophie Saskin, Michael L. Littman


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Successor Features Support Model-based and Model-free Reinforcement Learning


Jan 31, 2019
Lucas Lehnert, Michael L. Littman


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Theory of Minds: Understanding Behavior in Groups Through Inverse Planning


Jan 18, 2019
Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum

* published in AAAI 2019; Michael Shum and Max Kleiman-Weiner contributed equally 

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Mitigating Planner Overfitting in Model-Based Reinforcement Learning


Dec 03, 2018
Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman


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Towards a Simple Approach to Multi-step Model-based Reinforcement Learning


Oct 31, 2018
Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman


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Lipschitz Continuity in Model-based Reinforcement Learning


Jul 27, 2018
Kavosh Asadi, Dipendra Misra, Michael L. Littman

* Accepted for the 35th International Conference on Machine Learning (ICML 2018) 

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Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning


Jul 08, 2018
Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman

* Accepted at the FAIM workshop "Prediction and Generative Modeling in Reinforcement Learning", Stockholm, Sweden, 2018 

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Transfer with Model Features in Reinforcement Learning


Jul 04, 2018
Lucas Lehnert, Michael L. Littman


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Modeling Latent Attention Within Neural Networks


Dec 30, 2017
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman


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Learning Approximate Stochastic Transition Models


Oct 26, 2017
Yuhang Song, Christopher Grimm, Xianming Wang, Michael L. Littman


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Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting


Sep 19, 2017
Christopher Grimm, Yuhang Song, Michael L. Littman


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Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning


Jul 31, 2017
Lucas Lehnert, Stefanie Tellex, Michael L. Littman


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An Alternative Softmax Operator for Reinforcement Learning


Jun 14, 2017
Kavosh Asadi, Michael L. Littman


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Environment-Independent Task Specifications via GLTL


Apr 14, 2017
Michael L. Littman, Ufuk Topcu, Jie Fu, Charles Isbell, Min Wen, James MacGlashan


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Interactive Learning from Policy-Dependent Human Feedback


Jan 21, 2017
James MacGlashan, Mark K Ho, Robert Loftin, Bei Peng, David Roberts, Matthew E. Taylor, Michael L. Littman

* 7 pages, 2 figures 

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