Laskin, Michael

20 publications

ICLR 2023 In-Context Reinforcement Learning with Algorithm Distillation Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, Dj Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih

NeurIPSW 2023 Vision-Language Models as a Source of Rewards Kate Baumli, Satinder Singh, Feryal Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei M Zhang

ICLRW 2022 Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin, Pieter Abbeel, Alessandro Lazaric, Lerrel Pinto

ICLR 2022 Hierarchical Few-Shot Imitation with Skill Transition Models Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan, Pieter Abbeel, Michael Laskin

NeurIPSW 2022 In-Context Reinforcement Learning with Algorithm Distillation Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, Dj Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih

NeurIPSW 2022 In-Context Reinforcement Learning with Algorithm Distillation Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, Dj Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih

NeurIPS 2022 Unsupervised Reinforcement Learning with Contrastive Intrinsic Control Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel

NeurIPSW 2021 A Framework for Efficient Robotic Manipulation Albert Zhan, Ruihan Zhao, Lerrel Pinto, Pieter Abbeel, Michael Laskin

NeurIPSW 2021 Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL Catherine Cang, Aravind Rajeswaran, Pieter Abbeel, Michael Laskin

NeurIPSW 2021 CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel

ICMLW 2021 Data-Efficient Exploration with Self Play for Atari Michael Laskin, Catherine Cang, Ryan Rudes, Pieter Abbeel

ICMLW 2021 Decision Transformer: Reinforcement Learning via Sequence Modeling Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch

ICML 2021 Decoupling Representation Learning from Reinforcement Learning Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin

ICMLW 2021 Hierarchical Few-Shot Imitation with Skill Transition Models Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan, Pieter Abbeel, Michael Laskin

NeurIPSW 2021 Hierarchical Few-Shot Imitation with Skill Transition Models Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan, Pieter Abbeel, Michael Laskin

ICML 2021 SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel

CoRL 2021 Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback Xiaofei Wang, Kimin Lee, Kourosh Hakhamaneshi, Pieter Abbeel, Michael Laskin

NeurIPSW 2021 Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback Xiaofei Wang, Kimin Lee, Kourosh Hakhamaneshi, Pieter Abbeel, Michael Laskin

NeurIPSW 2021 URLB: Unsupervised Reinforcement Learning Benchmark Michael Laskin, Denis Yarats, Hao Liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, Pieter Abbeel

ICML 2020 CURL: Contrastive Unsupervised Representations for Reinforcement Learning Michael Laskin, Aravind Srinivas, Pieter Abbeel