Michael (misha) Laskin
Michael (misha) Laskin
Staff Research Scientist, DeepMind
Verified email at - Homepage
Cited by
Cited by
Decision transformer: Reinforcement learning via sequence modeling
L Chen, K Lu, A Rajeswaran, K Lee, A Grover, M Laskin, P Abbeel, ...
Advances in neural information processing systems 34, 15084-15097, 2021
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
M Laskin, A Srinivas, P Abbeel
Proceedings of the 37th International Conference on Machine Learning, Vienna …, 2020
Reinforcement learning with augmented data
M Laskin, K Lee, A Stooke, L Pinto, P Abbeel, A Srinivas
Advances in neural information processing systems 33, 19884-19895, 2020
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
Decoupling representation learning from reinforcement learning
A Stooke, K Lee, P Abbeel, M Laskin
International Conference on Machine Learning, 9870-9879, 2021
Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning
K Lee, M Laskin, A Srinivas, P Abbeel
International Conference on Machine Learning, 6131-6141, 2021
Fractional quantum Hall effect in a curved space: gravitational anomaly and electromagnetic response
T Can, M Laskin, P Wiegmann
Physical review letters 113 (4), 046803, 2014
Urlb: Unsupervised reinforcement learning benchmark
M Laskin, D Yarats, H Liu, K Lee, A Zhan, K Lu, C Cang, L Pinto, P Abbeel
arXiv preprint arXiv:2110.15191, 2021
Geometry of quantum Hall states: Gravitational anomaly and transport coefficients
T Can, M Laskin, PB Wiegmann
Annals of Physics 362, 752-794, 2015
A framework for efficient robotic manipulation
A Zhan, R Zhao, L Pinto, P Abbeel, M Laskin
Deep RL Workshop NeurIPS 2021, 2021
Don't change the algorithm, change the data: Exploratory data for offline reinforcement learning
D Yarats, D Brandfonbrener, H Liu, M Laskin, P Abbeel, A Lazaric, L Pinto
arXiv preprint arXiv:2201.13425, 2022
In-context reinforcement learning with algorithm distillation
M Laskin, L Wang, J Oh, E Parisotto, S Spencer, R Steigerwald, ...
arXiv preprint arXiv:2210.14215, 2022
Cic: Contrastive intrinsic control for unsupervised skill discovery
M Laskin, H Liu, XB Peng, D Yarats, A Rajeswaran, P Abbeel
arXiv preprint arXiv:2202.00161, 2022
Emergent conformal symmetry and geometric transport properties of quantum Hall states on singular surfaces
T Can, YH Chiu, M Laskin, P Wiegmann
Physical review letters 117 (26), 266803, 2016
Collective field theory for quantum Hall states
M Laskin, T Can, P Wiegmann
Physical Review B 92 (23), 235141, 2015
Sparse graphical memory for robust planning
S Emmons, A Jain, M Laskin, T Kurutach, P Abbeel, D Pathak
Advances in neural information processing systems 33, 5251-5262, 2020
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
Skill preferences: Learning to extract and execute robotic skills from human feedback
X Wang, K Lee, K Hakhamaneshi, P Abbeel, M Laskin
Conference on Robot Learning, 1259-1268, 2022
Hierarchical few-shot imitation with skill transition models
K Hakhamaneshi, R Zhao, A Zhan, P Abbeel, M Laskin
arXiv preprint arXiv:2107.08981, 2021
Behavioral priors and dynamics models: Improving performance and domain transfer in offline rl
C Cang, A Rajeswaran, P Abbeel, M Laskin
arXiv preprint arXiv:2106.09119, 2021
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