Littman, Michael

18 publications

ICML 2025 Knowledge Retention in Continual Model-Based Reinforcement Learning Haotian Fu, Yixiang Sun, Michael Littman, George Konidaris

NeurIPSW 2024 Knowledge Retention in Continual Model-Based Reinforcement Learning Haotian Fu, Yixiang Sun, Michael Littman, George Konidaris

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

AISTATS 2023 Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces Omer Gottesman, Kavosh Asadi, Cameron S. Allen, Samuel Lobel, George Konidaris, Michael Littman

AAAI 2023 Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable Cambridge Yang, Michael Littman, Michael Carbin

ICML 2023 Meta-Learning Parameterized Skills Haotian Fu, Shangqun Yu, Saket Tiwari, Michael Littman, George Konidaris

ICMLW 2023 Specifying Behavior Preference with Tiered Reward Functions Zhiyuan Zhou, Henry Sowerby, Michael Littman

NeurIPSW 2021 Bayesian Exploration for Lifelong Reinforcement Learning Haotian Fu, Shangqun Yu, Michael Littman, George Konidaris

AAAI 2020 Task Scoping for Efficient Planning in Open Worlds (Student Abstract) Nishanth Kumar, Michael Fishman, Natasha Danas, Stefanie Tellex, Michael Littman, George Konidaris

AISTATS 2020 Value Preserving State-Action Abstractions David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael Littman

ICML 2019 Finding Options That Minimize Planning Time Yuu Jinnai, David Abel, David Hershkowitz, Michael Littman, George Konidaris

ICML 2018 Lipschitz Continuity in Model-Based Reinforcement Learning Kavosh Asadi, Dipendra Misra, Michael Littman

ICML 2018 Policy and Value Transfer in Lifelong Reinforcement Learning David Abel, Yuu Jinnai, Sophie Yue Guo, George Konidaris, Michael Littman

ICML 2018 State Abstractions for Lifelong Reinforcement Learning David Abel, Dilip Arumugam, Lucas Lehnert, Michael Littman

ICML 2016 Near Optimal Behavior via Approximate State Abstraction David Abel, David Hershkowitz, Michael Littman

NeurIPS 2016 Showing Versus Doing: Teaching by Demonstration Mark K Ho, Michael Littman, James MacGlashan, Fiery Cushman, Joseph L Austerweil

ICML 2013 COCO-Q: Learning in Stochastic Games with Side Payments Eric Sodomka, Elizabeth Hilliard, Michael Littman, Amy Greenwald

ICML 2013 The Cross-Entropy Method Optimizes for Quantiles Sergiu Goschin, Ari Weinstein, Michael Littman