Sergey Levine

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Google ∙ berkeley college ∙ Stanford University

Assistant Professor at UC Berkeley

Online Meta-Learning

A central capability of intelligent systems is the ability to continuous...
02/22/2019 ∙ by Chelsea Finn, et al. ∙ 54 ∙ share

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Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Meta-reinforcement learning algorithms can enable robots to acquire new ...
10/24/2019 ∙ by Tianhe Yu, et al. ∙ 45 ∙ share

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Recurrent Independent Mechanisms

Learning modular structures which reflect the dynamics of the environmen...
09/24/2019 ∙ by Anirudh Goyal, et al. ∙ 35 ∙ share

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Entity Abstraction in Visual Model-Based Reinforcement Learning

This paper tests the hypothesis that modeling a scene in terms of entiti...
10/28/2019 ∙ by Rishi Veerapaneni, et al. ∙ 33 ∙ share

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RoboNet: Large-Scale Multi-Robot Learning

Robot learning has emerged as a promising tool for taming the complexity...
10/24/2019 ∙ by Sudeep Dasari, et al. ∙ 33 ∙ share

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Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

Deep reinforcement learning algorithms require large amounts of experien...
03/19/2019 ∙ by Kate Rakelly, et al. ∙ 32 ∙ share

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Learning to Walk via Deep Reinforcement Learning

Deep reinforcement learning suggests the promise of fully automated lear...
12/26/2018 ∙ by Tuomas Haarnoja, et al. ∙ 30 ∙ share

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Grasp2Vec: Learning Object Representations from Self-Supervised Grasping

Well structured visual representations can make robot learning faster an...
11/16/2018 ∙ by Eric Jang, et al. ∙ 26 ∙ share

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Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight

Deep reinforcement learning provides a promising approach for vision-bas...
02/11/2019 ∙ by Katie Kang, et al. ∙ 22 ∙ share

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Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

Adversarial learning methods have been proposed for a wide range of appl...
10/01/2018 ∙ by Xue Bin Peng, et al. ∙ 20 ∙ share

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Model-Based Reinforcement Learning for Atari

Model-free reinforcement learning (RL) can be used to learn effective po...
03/01/2019 ∙ by Łukasz Kaiser, et al. ∙ 20 ∙ share

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Learning Human Objectives by Evaluating Hypothetical Behavior

We seek to align agent behavior with a user's objectives in a reinforcem...
12/05/2019 ∙ by Siddharth Reddy, et al. ∙ 20 ∙ share

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Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations

Reinforcement learning (RL) for robotics is challenging due to the diffi...
05/31/2019 ∙ by Brijen Thananjeyan, et al. ∙ 19 ∙ share

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Meta-Learning without Memorization

The ability to learn new concepts with small amounts of data is a critic...
12/09/2019 ∙ by Mingzhang Yin, et al. ∙ 19 ∙ share

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Gradient Surgery for Multi-Task Learning

While deep learning and deep reinforcement learning (RL) systems have de...
01/19/2020 ∙ by Tianhe Yu, et al. ∙ 19 ∙ share

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InfoBot: Transfer and Exploration via the Information Bottleneck

A central challenge in reinforcement learning is discovering effective p...
01/30/2019 ∙ by Anirudh Goyal, et al. ∙ 18 ∙ share

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Wasserstein Dependency Measure for Representation Learning

Mutual information maximization has emerged as a powerful learning objec...
03/28/2019 ∙ by Sherjil Ozair, et al. ∙ 18 ∙ share

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ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

ROBEL is an open-source platform of cost-effective robots designed for r...
09/25/2019 ∙ by Michael Ahn, et al. ∙ 18 ∙ share

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Skew-Fit: State-Covering Self-Supervised Reinforcement Learning

In standard reinforcement learning, each new skill requires a manually-d...
03/08/2019 ∙ by Vitchyr H. Pong, et al. ∙ 16 ∙ share

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Morphology-Agnostic Visual Robotic Control

Existing approaches for visuomotor robotic control typically require cha...
12/31/2019 ∙ by Brian Yang, et al. ∙ 16 ∙ share

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Reasoning About Physical Interactions with Object-Oriented Prediction and Planning

Object-based factorizations provide a useful level of abstraction for in...
12/28/2018 ∙ by Michael Janner, et al. ∙ 14 ∙ share

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Data-efficient Learning of Morphology and Controller for a Microrobot

Robot design is often a slow and difficult process requiring the iterati...
05/03/2019 ∙ by Thomas Liao, et al. ∙ 14 ∙ share

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Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards

Connector insertion and many other tasks commonly found in modern manufa...
06/13/2019 ∙ by Gerrit Schoettler, et al. ∙ 14 ∙ share

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Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?

Hierarchical reinforcement learning has demonstrated significant success...
09/23/2019 ∙ by Ofir Nachum, et al. ∙ 14 ∙ share

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Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

We present relay policy learning, a method for imitation and reinforceme...
10/25/2019 ∙ by Abhishek Gupta, et al. ∙ 14 ∙ share

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Planning with Goal-Conditioned Policies

Planning methods can solve temporally extended sequential decision makin...
11/19/2019 ∙ by Soroush Nasiriany, et al. ∙ 14 ∙ share

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Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

We study the problem of representation learning in goal-conditioned hier...
10/02/2018 ∙ by Ofir Nachum, et al. ∙ 12 ∙ share

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One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks

We consider the problem of learning multi-stage vision-based tasks on a ...
10/25/2018 ∙ by Tianhe Yu, et al. ∙ 12 ∙ share

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Soft Actor-Critic Algorithms and Applications

Model-free deep reinforcement learning (RL) algorithms have been success...
12/13/2018 ∙ by Tuomas Haarnoja, et al. ∙ 12 ∙ share

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Plan Arithmetic: Compositional Plan Vectors for Multi-Task Control

Autonomous agents situated in real-world environments must be able to ma...
10/30/2019 ∙ by Coline Devin, et al. ∙ 12 ∙ share

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Learning Predictive Models From Observation and Interaction

Learning predictive models from interaction with the world allows an age...
12/30/2019 ∙ by Karl Schmeckpeper, et al. ∙ 12 ∙ share

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AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos

Robotic reinforcement learning (RL) holds the promise of enabling robots...
12/10/2019 ∙ by Laura Smith, et al. ∙ 11 ∙ share

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Automatically Composing Representation Transformations as a Means for Generalization

How can we build a learner that can capture the essence of what makes a ...
07/12/2018 ∙ by Michael B. Chang, et al. ∙ 10 ∙ share

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Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty

Deep learning provides a powerful tool for machine perception when the o...
12/27/2018 ∙ by Rowan McAllister, et al. ∙ 10 ∙ share

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Learning Powerful Policies by Using Consistent Dynamics Model

Model-based Reinforcement Learning approaches have the promise of being ...
06/11/2019 ∙ by Shagun Sodhani, et al. ∙ 10 ∙ share

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Time-Agnostic Prediction: Predicting Predictable Video Frames

Prediction is arguably one of the most basic functions of an intelligent...
08/23/2018 ∙ by Dinesh Jayaraman, et al. ∙ 8 ∙ share

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SFV: Reinforcement Learning of Physical Skills from Videos

Data-driven character animation based on motion capture can produce high...
10/08/2018 ∙ by Xue Bin Peng, et al. ∙ 8 ∙ share

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Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation

A general-purpose intelligent robot must be able to learn autonomously a...
10/16/2018 ∙ by Gregory Kahn, et al. ∙ 8 ∙ share

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Guiding Policies with Language via Meta-Learning

Behavioral skills or policies for autonomous agents are conventionally l...
11/19/2018 ∙ by John D. Co-Reyes, et al. ∙ 8 ∙ share

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Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks

Real world data, especially in the domain of robotics, is notoriously co...
12/18/2018 ∙ by Stephen James, et al. ∙ 8 ∙ share

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Guided Meta-Policy Search

Reinforcement learning (RL) algorithms have demonstrated promising resul...
04/01/2019 ∙ by Russell Mendonca, et al. ∙ 8 ∙ share

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End-to-End Robotic Reinforcement Learning without Reward Engineering

The combination of deep neural network models and reinforcement learning...
04/16/2019 ∙ by Avi Singh, et al. ∙ 8 ∙ share

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SMiRL: Surprise Minimizing RL in Dynamic Environments

All living organisms struggle against the forces of nature to carve out ...
12/11/2019 ∙ by Glen Berseth, et al. ∙ 8 ∙ share

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Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

Off-policy reinforcement learning aims to leverage experience collected ...
06/03/2019 ∙ by Aviral Kumar, et al. ∙ 7 ∙ share

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Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

Deep reinforcement learning (RL) algorithms can use high-capacity deep n...
07/01/2019 ∙ by Alex X. Lee, et al. ∙ 7 ∙ share

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Unsupervised Curricula for Visual Meta-Reinforcement Learning

In principle, meta-reinforcement learning algorithms leverage experience...
12/09/2019 ∙ by Allan Jabri, et al. ∙ 7 ∙ share

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Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings

In this work, we take a representation learning perspective on hierarchi...
06/07/2018 ∙ by John D. Co-Reyes, et al. ∙ 6 ∙ share

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More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch

For humans, the process of grasping an object relies heavily on rich tac...
05/28/2018 ∙ by Roberto Calandra, et al. ∙ 6 ∙ share

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EMI: Exploration with Mutual Information Maximizing State and Action Embeddings

Policy optimization struggles when the reward feedback signal is very sp...
10/02/2018 ∙ by HyoungSeok Kim, et al. ∙ 6 ∙ share

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From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following

Reinforcement learning is a promising framework for solving control prob...
02/20/2019 ∙ by Justin Fu, et al. ∙ 6 ∙ share

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Sergey Levine

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