michael (misha) laskin

I'm a postdoc at UC Berkeley in the BAIR lab, where I work on deep unsupervised and reinforcement learning with Pieter Abbeel. I received my PhD in theoretical physics from the University of Chicago, where I worked on statisical quantum physics with Paul Wiegmann. During my PhD I received the Bloomenthal Fellowship, awarded to the best graduate student in theoretical physics. In between my PhD and postdoc I started a company - Claire AI - which made AI products for the retail sector and went through Y Combinator (W17). My co-founder and I made the Forbes 30 Under 30 list in 2017 for our work in retail and e-commerce. Before that, I received a BS from Yale University where I studied physics and literature.

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AI Research

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A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel
In Submission, 2020
paper / code
Developed a unified framework for utilizing ensembles to greatly stabilize training of both state-based and pixel-based RL algorithms.
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Reinforcement Learning with Augmented Data

Michael Laskin*, Kimin Lee*, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas
In Submission, 2020
paper / code / twitter / press
First extensive study of image augmentation in the RL setting. Showed that simple RL algorithms with augmented data achieve SOTA results on many common RL benchmarks.
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CURL: Contrastive Unsupervised Representations for Reinforcement Learning

Michael Laskin*, Aravind Srinivas*, Pieter Abbeel
ICML, 2020
paper / code / twitter
Showed for the first time that RL from pixels can be as efficient as RL from state by leveraging unsupervised contrastive representations.
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Sparse Graphical Memory for Robust Planning

Michael Laskin*, Scott Emmons*, Ajay Jain*, Thanard Kurutach, Pieter Abbeel, Deepak Pathak
In Submission, 2020
paper / video / code
Introduced novel state aggregation criterium - two-way consistency (TWC) - and utilized it to make any semi-parametric graphical method more robust. Proved theoretically that TWC enables near-optimal search.
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Discrete Representation Learning for Goal-Conditioned Visual Reinforcement Learning

Michael Laskin, Thanard Kurutach, Pieter Abbeel
NeurIPS Deep Reinforcement Learning Workshop, 2019
Discrete representations help reduce the size of the encoded observation space. We showed how utilizing a discrete bottleneck can improve goal-conditioned RL from pixels.

Physics Research

During my physics PhD I studied many-body quantum systems such as the Fractional Quantum Hall Effect, and discovered a universal topological characteristic of such states.

Note: in theoretical physics alphabetical order is the usual convention for authorship.

Emergent conformal symmetry and geometric transport properties of quantum Hall states on singular surfaces

T. Can, Y.H. Chiu, M. Laskin, P. Wiegmann
Physical review letters 117 (26), 266803, 2016

Population of the giant pairing vibration

M. Laskin, R.F. Casten, A.O. Macchiavelli, R.M. Clark, D. Bucurescu
Physical Review C 93 (3), 034321, 2016

Collective field theory for quantum Hall states

T. Can, M. Laskin, P. Wiegmann
Physical Review B 92 (23), 235141 , 2015

Geometry of quantum Hall states: Gravitational anomaly and transport coefficients

T. Can, M. Laskin, P. Wiegmann
Annals of Physics 362, 752-794, 2015

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

Field Theory for Fractional Quantum Hall States

T. Can, M. Laskin, P. Wiegmann
arXiv preprint arXiv:1412.8716, 2014

Some Aspects of the Giant-Pairing Vibration

A.O. Macchiavelli, R.M. Clark, M. Laskin, R.F. Casten
Bulletin of the American Physical Society, 2012

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