Benjamin Eysenbach

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Google ∙ MIT ∙ Carnegie Mellon University

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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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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f-IRL: Inverse Reinforcement Learning via State Marginal Matching

Imitation learning is well-suited for robotic tasks where it is difficul...
11/09/2020 ∙ by Tianwei Ni, et al. ∙ 0 ∙ share

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Learning to be Safe: Deep RL with a Safety Critic

Safety is an essential component for deploying reinforcement learning (R...
10/27/2020 ∙ by Krishnan Srinivasan, et al. ∙ 3 ∙ share

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Interactive Visualization for Debugging RL

Visualization tools for supervised learning allow users to interpret, in...
08/14/2020 ∙ by Shuby Deshpande, et al. ∙ 0 ∙ 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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Weakly-Supervised Reinforcement Learning for Controllable Behavior

Reinforcement learning (RL) is a powerful framework for learning to take...
04/06/2020 ∙ by Lisa Lee, et al. ∙ 11 ∙ share

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Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement

Multi-task reinforcement learning (RL) aims to simultaneously learn poli...
02/25/2020 ∙ by Benjamin Eysenbach, et al. ∙ 11 ∙ share

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Learning To Reach Goals Without Reinforcement Learning

Imitation learning algorithms provide a simple and straightforward appro...
12/12/2019 ∙ by Dibya Ghosh, et al. ∙ 0 ∙ share

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

Experimentally, it has been observed that humans and animals often make ...
10/04/2019 ∙ by Benjamin Eysenbach, et al. ∙ 0 ∙ share

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Efficient Exploration via State Marginal Matching

To solve tasks with sparse rewards, reinforcement learning algorithms mu...
06/12/2019 ∙ by Lisa Lee, et al. ∙ 0 ∙ share

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Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

The history of learning for control has been an exciting back and forth ...
06/12/2019 ∙ by Benjamin Eysenbach, et al. ∙ 5 ∙ share

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Unsupervised Meta-Learning for Reinforcement Learning

Meta-learning is a powerful tool that builds on multi-task learning to l...
06/12/2018 ∙ by Abhishek Gupta, et al. ∙ 0 ∙ 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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Diversity is All You Need: Learning Skills without a Reward Function

Intelligent creatures can explore their environments and learn useful sk...
02/16/2018 ∙ by Benjamin Eysenbach, et al. ∙ 0 ∙ share

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

Deep reinforcement learning algorithms can learn complex behavioral skil...
11/18/2017 ∙ by Benjamin Eysenbach, et al. ∙ 0 ∙ share

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Who is Mistaken?

Recognizing when people have false beliefs is crucial for understanding ...
12/04/2016 ∙ by Benjamin Eysenbach, et al. ∙ 0 ∙ share

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Benjamin Eysenbach

Featured Co-authors

Sergey Levine

Pieter Abbeel

Ruslan Salakhutdinov

Antonio Torralba

Chelsea Finn

Abhishek Gupta

Jeff Schneider

Eric Xing

Carl Vondrick

Jie Tan

Shixiang Gu

Model-Based Visual Planning with Self-Supervised Functional Distances

ViNG: Learning Open-World Navigation with Visual Goals

C-Learning: Learning to Achieve Goals via Recursive Classification

f-IRL: Inverse Reinforcement Learning via State Marginal Matching

Learning to be Safe: Deep RL with a Safety Critic

Interactive Visualization for Debugging RL

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

Weakly-Supervised Reinforcement Learning for Controllable Behavior

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement

Learning To Reach Goals Without Reinforcement Learning

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

Efficient Exploration via State Marginal Matching

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

Unsupervised Meta-Learning for Reinforcement Learning

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

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

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

Who is Mistaken?