Benjamin Eysenbach

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Google ∙ MIT ∙ Carnegie Mellon University

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

Eric Xing

Jeff Schneider

Carl Vondrick

Shixiang Gu

Julian Ibarz

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?