Benjamin Eysenbach

research

∙ 06/15/2022

Contrastive Learning as Goal-Conditioned Reinforcement Learning

In reinforcement learning (RL), it is easier to solve a task if given a ...

Benjamin Eysenbach, et al. ∙

research

∙ 06/07/2022

Imitating Past Successes can be Very Suboptimal

Prior work has proposed a simple strategy for reinforcement learning (RL...

Benjamin Eysenbach, et al. ∙

research

∙ 06/03/2022

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions

Supervised learning methods trained with maximum likelihood objectives o...

Amrith Setlur, et al. ∙

research

∙ 12/20/2021

RvS: What is Essential for Offline RL via Supervised Learning?

Recent work has shown that supervised learning alone, without temporal d...

Scott Emmons, et al. ∙

research

∙ 10/22/2021

C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks

Goal-conditioned reinforcement learning (RL) can solve tasks in a wide r...

Tianjun Zhang, et al. ∙

research

∙ 10/11/2021

Recurrent Model-Free RL is a Strong Baseline for Many POMDPs

Many problems in RL, such as meta RL, robust RL, and generalization in R...

Tianwei Ni, et al. ∙

research

∙ 10/06/2021

Mismatched No More: Joint Model-Policy Optimization for Model-Based RL

Many model-based reinforcement learning (RL) methods follow a similar te...

Benjamin Eysenbach, et al. ∙

research

∙ 10/06/2021

The Information Geometry of Unsupervised Reinforcement Learning

How can a reinforcement learning (RL) agent prepare to solve downstream ...

Benjamin Eysenbach, et al. ∙

research

∙ 09/07/2021

Robust Predictable Control

Many of the challenges facing today's reinforcement learning (RL) algori...

Benjamin Eysenbach, et al. ∙

research

∙ 04/15/2021

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills

We consider the problem of learning useful robotic skills from previousl...

Yevgen Chebotar, et al. ∙

research

∙ 04/12/2021

RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models

We describe a robotic learning system for autonomous navigation in diver...

Dhruv Shah, et al. ∙

research

∙ 03/23/2021

Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification

In the standard Markov decision process formalism, users specify tasks b...

Benjamin Eysenbach, et al. ∙

research

∙ 03/10/2021

Maximum Entropy RL (Provably) Solves Some Robust RL Problems

Many potential applications of reinforcement learning (RL) require guara...

Benjamin Eysenbach, et al. ∙

research

∙ 12/30/2020

Model-Based Visual Planning with Self-Supervised Functional Distances

A generalist robot must be able to complete a variety of tasks in its en...

Stephen Tian, et al. ∙

research

∙ 12/17/2020

ViNG: Learning Open-World Navigation with Visual Goals

We propose a learning-based navigation system for reaching visually indi...

Dhruv Shah, et al. ∙

research

∙ 11/17/2020

C-Learning: Learning to Achieve Goals via Recursive Classification

We study the problem of predicting and controlling the future state dist...

Benjamin Eysenbach, et al. ∙

research

∙ 11/09/2020

f-IRL: Inverse Reinforcement Learning via State Marginal Matching

Imitation learning is well-suited for robotic tasks where it is difficul...

Tianwei Ni, et al. ∙

research

∙ 10/27/2020

Learning to be Safe: Deep RL with a Safety Critic

Safety is an essential component for deploying reinforcement learning (R...

Krishnan Srinivasan, et al. ∙

research

∙ 08/14/2020

Interactive Visualization for Debugging RL

Visualization tools for supervised learning allow users to interpret, in...

Shuby Deshpande, et al. ∙

research

∙ 06/24/2020

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

We propose a simple, practical, and intuitive approach for domain adapta...

Benjamin Eysenbach, et al. ∙

research

∙ 04/06/2020

Weakly-Supervised Reinforcement Learning for Controllable Behavior

Reinforcement learning (RL) is a powerful framework for learning to take...

Lisa Lee, et al. ∙

research

∙ 02/25/2020

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement

Multi-task reinforcement learning (RL) aims to simultaneously learn poli...

Benjamin Eysenbach, et al. ∙

research

∙ 12/12/2019

Learning To Reach Goals Without Reinforcement Learning

Imitation learning algorithms provide a simple and straightforward appro...

Dibya Ghosh, et al. ∙

research

∙ 10/04/2019

If MaxEnt RL is the Answer, What is the Question?

Experimentally, it has been observed that humans and animals often make ...

Benjamin Eysenbach, et al. ∙

research

∙ 06/12/2019

Efficient Exploration via State Marginal Matching

To solve tasks with sparse rewards, reinforcement learning algorithms mu...

Lisa Lee, et al. ∙

research

∙ 06/12/2019

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

The history of learning for control has been an exciting back and forth ...

Benjamin Eysenbach, et al. ∙

research

∙ 06/12/2018

Unsupervised Meta-Learning for Reinforcement Learning

Meta-learning is a powerful tool that builds on multi-task learning to l...

Abhishek Gupta, et al. ∙

research

∙ 06/07/2018

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

In this work, we take a representation learning perspective on hierarchi...

John D. Co-Reyes, et al. ∙

research

∙ 02/16/2018

Diversity is All You Need: Learning Skills without a Reward Function

Intelligent creatures can explore their environments and learn useful sk...

Benjamin Eysenbach, et al. ∙

research

∙ 11/18/2017

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning

Deep reinforcement learning algorithms can learn complex behavioral skil...

Benjamin Eysenbach, et al. ∙

research

∙ 12/04/2016

Who is Mistaken?

Recognizing when people have false beliefs is crucial for understanding ...

Benjamin Eysenbach, et al. ∙

Benjamin Eysenbach

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