b'Sergey Levine'

SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning

As learning-based approaches progress towards automating robot controlle...
01/15/2021 ∙ by Yifeng Jiang, et al. ∙ 0 ∙ share

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Evolving Reinforcement Learning Algorithms

We propose a method for meta-learning reinforcement learning algorithms ...
01/08/2021 ∙ by John D. Co-Reyes, et al. ∙ 28 ∙ share

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Model-Based Visual Planning with Self-Supervised Functional Distances

A generalist robot must be able to complete a variety of tasks in its en...
12/30/2020 ∙ by Stephen Tian, et al. ∙ 13 ∙ share

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ViNG: Learning Open-World Navigation with Visual Goals

We propose a learning-based navigation system for reaching visually indi...
12/17/2020 ∙ by Dhruv Shah, et al. ∙ 15 ∙ share

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Variable-Shot Adaptation for Online Meta-Learning

Few-shot meta-learning methods consider the problem of learning new task...
12/14/2020 ∙ by Tianhe Yu, et al. ∙ 5 ∙ share

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WILDS: A Benchmark of in-the-Wild Distribution Shifts

Distribution shifts can cause significant degradation in a broad range o...
12/14/2020 ∙ by Pang Wei Koh, et al. ∙ 10 ∙ share

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Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) methods have shown strong samp...
12/08/2020 ∙ by Mohammad Babaeizadeh, et al. ∙ 7 ∙ share

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Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

A wide range of reinforcement learning (RL) problems - including robustn...
12/03/2020 ∙ by Michael Dennis, et al. ∙ 0 ∙ share

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Parrot: Data-Driven Behavioral Priors for Reinforcement Learning

Reinforcement learning provides a general framework for flexible decisio...
11/19/2020 ∙ by Avi Singh, et al. ∙ 18 ∙ share

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C-Learning: Learning to Achieve Goals via Recursive Classification

We study the problem of predicting and controlling the future state dist...
11/17/2020 ∙ by Benjamin Eysenbach, et al. ∙ 12 ∙ share

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Reinforcement Learning with Videos: Combining Offline Observations with Interaction

Reinforcement learning is a powerful framework for robots to acquire ski...
11/12/2020 ∙ by Karl Schmeckpeper, et al. ∙ 15 ∙ share

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Continual Learning of Control Primitives: Skill Discovery via Reset-Games

Reinforcement learning has the potential to automate the acquisition of ...
11/10/2020 ∙ by Kelvin Xu, et al. ∙ 0 ∙ share

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Amortized Conditional Normalized Maximum Likelihood

While deep neural networks provide good performance for a range of chall...
11/05/2020 ∙ by Aurick Zhou, et al. ∙ 0 ∙ share

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Rearrangement: A Challenge for Embodied AI

We describe a framework for research and evaluation in Embodied AI. Our ...
11/03/2020 ∙ by Dhruv Batra, et al. ∙ 2 ∙ share

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COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning

Reinforcement learning has been applied to a wide variety of robotics pr...
10/27/2020 ∙ by Avi Singh, et al. ∙ 0 ∙ share

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Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

We identify an implicit under-parameterization phenomenon in value-based...
10/27/2020 ∙ by Aviral Kumar, et al. ∙ 0 ∙ share

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Conservative Safety Critics for Exploration

Safe exploration presents a major challenge in reinforcement learning (R...
10/27/2020 ∙ by Homanga Bharadhwaj, et al. ∙ 0 ∙ share

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γ-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction

We introduce the γ-model, a predictive model of environment dynamics wit...
10/27/2020 ∙ by Michael Janner, et al. ∙ 15 ∙ share

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One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL

While reinforcement learning algorithms can learn effective policies for...
10/27/2020 ∙ by Saurabh Kumar, et al. ∙ 4 ∙ share

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MELD: Meta-Reinforcement Learning from Images via Latent State Models

Meta-reinforcement learning algorithms can enable autonomous agents, suc...
10/26/2020 ∙ by Tony Z. Zhao, et al. ∙ 12 ∙ share

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OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning

Reinforcement learning (RL) has achieved impressive performance in a var...
10/26/2020 ∙ by Anurag Ajay, et al. ∙ 0 ∙ share

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LaND: Learning to Navigate from Disengagements

Consistently testing autonomous mobile robots in real world scenarios is...
10/09/2020 ∙ by Gregory Kahn, et al. ∙ 1 ∙ share

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Learning Social Learning

Social learning is a key component of human and animal intelligence. By ...
10/01/2020 ∙ by Kamal Ndousse, et al. ∙ 35 ∙ share

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Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings

Reinforcement learning (RL) in real-world safety-critical target setting...
08/15/2020 ∙ by Jesse Zhang, et al. ∙ 7 ∙ share

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Offline Meta-Reinforcement Learning with Advantage Weighting

Massive datasets have proven critical to successfully applying deep lear...
08/13/2020 ∙ by Eric Mitchell, et al. ∙ 31 ∙ share

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Assisted Perception: Optimizing Observations to Communicate State

We aim to help users estimate the state of the world in tasks like robot...
08/06/2020 ∙ by Siddharth Reddy, et al. ∙ 6 ∙ share

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Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift

A fundamental assumption of most machine learning algorithms is that the...
07/06/2020 ∙ by Marvin Zhang, et al. ∙ 5 ∙ share

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Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions

This paper seeks to establish a framework for directing a society of sim...
07/05/2020 ∙ by Michael Chang, et al. ∙ 0 ∙ share

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Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems

Modeling a structured, dynamic environment like a video game requires ke...
06/29/2020 ∙ by Anirudh Goyal, et al. ∙ 0 ∙ share

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Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Out-of-training-distribution (OOD) scenarios are a common challenge of l...
06/26/2020 ∙ by Angelos Filos, et al. ∙ 14 ∙ share

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Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

We propose a simple, practical, and intuitive approach for domain adapta...
06/24/2020 ∙ by Benjamin Eysenbach, et al. ∙ 15 ∙ share

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Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors

The ability to predict and plan into the future is fundamental for agent...
06/23/2020 ∙ by Karl Pertsch, et al. ∙ 8 ∙ share

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Simple and Effective VAE Training with Calibrated Decoders

Variational autoencoders (VAEs) provide an effective and simple method f...
06/23/2020 ∙ by Oleh Rybkin, et al. ∙ 22 ∙ share

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Ecological Reinforcement Learning

Much of the current work on reinforcement learning studies episodic sett...
06/22/2020 ∙ by John D. Co-Reyes, et al. ∙ 50 ∙ share

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Learning Invariant Representations for Reinforcement Learning without Reconstruction

We study how representation learning can accelerate reinforcement learni...
06/18/2020 ∙ by Amy Zhang, et al. ∙ 9 ∙ share

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Accelerating Online Reinforcement Learning with Offline Datasets

Reinforcement learning provides an appealing formalism for learning cont...
06/16/2020 ∙ by Ashvin Nair, et al. ∙ 0 ∙ share

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RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real

Deep neural network based reinforcement learning (RL) can learn appropri...
06/16/2020 ∙ by Kanishka Rao, et al. ∙ 10 ∙ share

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Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling

Reinforcement learning algorithms can acquire policies for complex tasks...
06/12/2020 ∙ by Russell Mendonca, et al. ∙ 41 ∙ share

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Conservative Q-Learning for Offline Reinforcement Learning

Effectively leveraging large, previously collected datasets in reinforce...
06/08/2020 ∙ by Aviral Kumar, et al. ∙ 0 ∙ share

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MOPO: Model-based Offline Policy Optimization

Offline reinforcement learning (RL) refers to the problem of learning po...
05/27/2020 ∙ by Tianhe Yu, et al. ∙ 15 ∙ share

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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

In this tutorial article, we aim to provide the reader with the conceptu...
05/04/2020 ∙ by Sergey Levine, et al. ∙ 28 ∙ share

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Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks

Robotic insertion tasks are characterized by contact and friction mechan...
04/29/2020 ∙ by Gerrit Schoettler, et al. ∙ 0 ∙ share

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Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning

Reinforcement learning provides a general framework for learning robotic...
04/27/2020 ∙ by Archit Sharma, et al. ∙ 0 ∙ share

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The Ingredients of Real-World Robotic Reinforcement Learning

The success of reinforcement learning for real world robotics has been, ...
04/27/2020 ∙ by Henry Zhu, et al. ∙ 14 ∙ share

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The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget

In many applications, it is desirable to extract only the relevant infor...
04/24/2020 ∙ by Anirudh Goyal, et al. ∙ 0 ∙ share

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Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads

Transporting suspended payloads is challenging for autonomous aerial veh...
04/23/2020 ∙ by Suneel Belkhale, et al. ∙ 1 ∙ share

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Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation

One of the great promises of robot learning systems is that they will be...
04/21/2020 ∙ by Ryan Julian, et al. ∙ 29 ∙ share

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D4RL: Datasets for Deep Data-Driven Reinforcement Learning

The offline reinforcement learning (RL) problem, also referred to as bat...
04/15/2020 ∙ by Justin Fu, et al. ∙ 41 ∙ share

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Datasets for Data-Driven Reinforcement Learning

The offline reinforcement learning (RL) problem, also referred to as bat...
04/15/2020 ∙ by Justin Fu, et al. ∙ 6 ∙ share

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Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

We study reinforcement learning in settings where sampling an action fro...
04/13/2020 ∙ by Ted Xiao, et al. ∙ 17 ∙ share

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Sergey Levine

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