465 citations · 1.5k across the 21 of their papers we have counts for
21 papers
In-context Reinforcement Learning with Algorithm Distillation
Michael Laskin, Luyu Wang, Junhyuk Oh +11
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal seque…
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
Michael Laskin, Hao Liu, Xue Bin Peng +3
We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between state-transitions and latent skill vec…
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning
Denis Yarats, David Brandfonbrener, Hao Liu +4
Recent progress in deep learning has relied on access to large and diverse datasets. Such data-driven progress has been less evident in offline reinforcement learning (RL), because…
URLB: Unsupervised Reinforcement Learning Benchmark
Michael Laskin, Denis Yarats, Hao Liu +6
Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks. Yet training generalist agents that can quickly adapt to…
Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback
Xiaofei Wang, Kimin Lee, Kourosh Hakhamaneshi +2
A promising approach to solving challenging long-horizon tasks has been to extract behavior priors (skills) by fitting generative models to large offline datasets of demonstrations…
Hierarchical Few-Shot Imitation with Skill Transition Models
Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan +2
A desirable property of autonomous agents is the ability to both solve long-horizon problems and generalize to unseen tasks. Recent advances in data-driven skill learning have show…
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran +6
We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer ar…
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL
Catherine Cang, Aravind Rajeswaran, Pieter Abbeel +1
Offline Reinforcement Learning (RL) aims to extract near-optimal policies from imperfect offline data without additional environment interactions. Extracting policies from diverse…
Reinforcement Learning with Latent Flow
Wenling Shang, Xiaofei Wang, Aravind Srinivas +4
Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such informati…
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation
Albert Zhan, Ruihan Zhao, Lerrel Pinto +2
Recent advances in unsupervised representation learning significantly improved the sample efficiency of training Reinforcement Learning policies in simulated environments. However,…
Parallel Training of Deep Networks with Local Updates
Michael Laskin, Luke Metz, Seth Nabarro +5
Deep learning models trained on large data sets have been widely successful in both vision and language domains. As state-of-the-art deep learning architectures have continued to g…
Decoupling Representation Learning from Reinforcement Learning
Adam Stooke, Kimin Lee, Pieter Abbeel +1
In an effort to overcome limitations of reward-driven feature learning in deep reinforcement learning (RL) from images, we propose decoupling representation learning from policy le…
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Kimin Lee, Michael Laskin, Aravind Srinivas +1
Off-policy deep reinforcement learning (RL) has been successful in a range of challenging domains. However, standard off-policy RL algorithms can suffer from several issues, such a…
Reinforcement Learning with Augmented Data
Michael Laskin, Kimin Lee, Adam Stooke +3
Learning from visual observations is a fundamental yet challenging problem in Reinforcement Learning (RL). Although algorithmic advances combined with convolutional neural networks…
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Aravind Srinivas, Michael Laskin, Pieter Abbeel
We present CURL: Contrastive Unsupervised Representations for Reinforcement Learning. CURL extracts high-level features from raw pixels using contrastive learning and performs off-…
Sparse Graphical Memory for Robust Planning
Scott Emmons, Ajay Jain, Michael Laskin +3
To operate effectively in the real world, agents should be able to act from high-dimensional raw sensory input such as images and achieve diverse goals across long time-horizons. C…
Roof Age Determination for the Automated Site-Selection of Rooftop Solar
Chris Heinrich, Michael Laskin, Simas Glinskis +1
Rooftop solar is one of the most promising tools for drawing down greenhouse gas (GHG) emissions and is cost-competitive with fossil fuels in many areas of the world today. One of…
Emergent Conformal Symmetry of Quantum Hall States on Singular surfaces
M. Laskin, Y. H. Chiu, T. Can +1
We show that quantum Hall states on surfaces with conical singularities behave as conformal primaries near the singular points, with a conformal dimension controlled by the gravita…
Collective Field Theory for Quantum Hall States
M. Laskin, T. Can, P. Wiegmann
We develop a collective field theory for fractional quantum Hall (FQH) states. We show that in the leading approximation for a large number of particles, the properties of Laughlin…
Geometry of Quantum Hall States: Gravitational Anomaly and Kinetic Coefficients
T. Can, M. Laskin, P. Wiegmann
We show that universal transport coefficients of the fractional quantum Hall effect (FQHE) can be understood as a response to variations of spatial geometry. Some transport propert…
Fractional Quantum Hall Effect in a Curved Space: Gravitational Anomaly and Electromagnetic Response
T. Can, M. Laskin, P. Wiegmann
We develop a general method to compute correlation functions of fractional quantum Hall (FQH) states on a curved space. In a curved space, local transformation properties of FQH st…