Benjamin Eysenbach

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 78

Top Topics

Predictive Coding

Reinforcement Learning

Reward Function

Top Venues

Publications

Vivek Myers, Chongyi Zheng, Anca D. Dragan, Sergey Levine, Benjamin Eysenbach
Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making. CoRR (2024)
Chongyi Zheng, Benjamin Eysenbach, Homer Rich Walke, Patrick Yin, Kuan Fang, Ruslan Salakhutdinov, Sergey Levine
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data. ICLR (2024)
Raj Ghugare, Matthieu Geist, Glen Berseth, Benjamin Eysenbach
Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View. ICLR (2024)
Raj Ghugare, Matthieu Geist, Glen Berseth, Benjamin Eysenbach
Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View. CoRR (2024)
Ifigeneia Apostolopoulou, Benjamin Eysenbach, Frank Nielsen, Artur Dubrawski
A Rate-Distortion View of Uncertainty Quantification. CoRR (2024)
Benjamin Eysenbach, Vivek Myers, Ruslan Salakhutdinov, Sergey Levine
Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference. CoRR (2024)
Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon
Bridging State and History Representations: Understanding Self-Predictive RL. ICLR (2024)
Chongyi Zheng, Ruslan Salakhutdinov, Benjamin Eysenbach
Contrastive Difference Predictive Coding. ICLR (2024)
Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Xiangyu Liu, Benjamin Eysenbach, Tuomas Sandholm, Furong Huang, Stephen Marcus McAleer
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations. ICLR (2024)
Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon
Bridging State and History Representations: Understanding Self-Predictive RL. CoRR (2024)
Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon
When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment. CoRR (2023)
Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine
HIQL: Offline Goal-Conditioned RL with Latent States as Actions. CoRR (2023)
Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. ICLR (2023)
Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine
HIQL: Offline Goal-Conditioned RL with Latent States as Actions. NeurIPS (2023)
Chongyi Zheng, Benjamin Eysenbach, Homer Walke, Patrick Yin, Kuan Fang, Ruslan Salakhutdinov, Sergey Levine
Stabilizing Contrastive RL: Techniques for Offline Goal Reaching. CoRR (2023)
Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov
A Connection between One-Step RL and Critic Regularization in Reinforcement Learning. ICML (2023)
Bogdan Mazoure, Benjamin Eysenbach, Ofir Nachum, Jonathan Tompson
Contrastive Value Learning: Implicit Models for Simple Offline RL. CoRL (2023)
Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon
When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment. NeurIPS (2023)
Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov
A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning. CoRR (2023)
Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn
Contrastive Example-Based Control. CoRR (2023)
Kyle Beltran Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn
Contrastive Example-Based Control. L4DC (2023)
Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. CoRR (2023)
Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Russ Salakhutdinov
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective. ICLR (2023)
Chongyi Zheng, Ruslan Salakhutdinov, Benjamin Eysenbach
Contrastive Difference Predictive Coding. CoRR (2023)
Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. NeurIPS (2022)
Bogdan Mazoure, Benjamin Eysenbach, Ofir Nachum, Jonathan Tompson
Contrastive Value Learning: Implicit Models for Simple Offline RL. CoRR (2022)
Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov
Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs. ICML (2022)
Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov
Mismatched No More: Joint Model-Policy Optimization for Model-Based RL. NeurIPS (2022)
Benjamin Eysenbach, Tianjun Zhang, Ruslan Salakhutdinov, Sergey Levine
Contrastive Learning as Goal-Conditioned Reinforcement Learning. CoRR (2022)
Benjamin Eysenbach, Sergey Levine
Maximum Entropy RL (Provably) Solves Some Robust RL Problems. ICLR (2022)
Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective. CoRR (2022)
Benjamin Eysenbach, Soumith Udatha, Sergey Levine, Ruslan Salakhutdinov
Imitating Past Successes can be Very Suboptimal. CoRR (2022)
Benjamin Eysenbach, Soumith Udatha, Russ Salakhutdinov, Sergey Levine
Imitating Past Successes can be Very Suboptimal. NeurIPS (2022)
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
The Information Geometry of Unsupervised Reinforcement Learning. ICLR (2022)
Yiding Jiang, Evan Zheran Liu, Benjamin Eysenbach, Zico Kolter, Chelsea Finn
Learning Options via Compression. CoRR (2022)
Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks. ICLR (2022)
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine
RvS: What is Essential for Offline RL via Supervised Learning? ICLR (2022)
Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. CoRR (2022)
Yiding Jiang, Evan Zheran Liu, Benjamin Eysenbach, J. Zico Kolter, Chelsea Finn
Learning Options via Compression. NeurIPS (2022)
Benjamin Eysenbach, Tianjun Zhang, Sergey Levine, Ruslan Salakhutdinov
Contrastive Learning as Goal-Conditioned Reinforcement Learning. NeurIPS (2022)
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine
RvS: What is Essential for Offline RL via Supervised Learning? CoRR (2021)
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
C-Learning: Learning to Achieve Goals via Recursive Classification. ICLR (2021)
Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov
Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification. CoRR (2021)
Benjamin Eysenbach, Sergey Levine
Maximum Entropy RL (Provably) Solves Some Robust RL Problems. CoRR (2021)
Dhruv Shah, Benjamin Eysenbach, Nicholas Rhinehart, Sergey Levine
RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models. CoRR (2021)
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
The Information Geometry of Unsupervised Reinforcement Learning. CoRR (2021)
Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine
ViNG: Learning Open-World Navigation with Visual Goals. ICRA (2021)
Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Manon Devin, Benjamin Eysenbach, Sergey Levine
Learning to Reach Goals via Iterated Supervised Learning. ICLR (2021)
Dhruv Shah, Benjamin Eysenbach, Nicholas Rhinehart, Sergey Levine
Rapid Exploration for Open-World Navigation with Latent Goal Models. CoRL (2021)
Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks. CoRR (2021)
Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov
Mismatched No More: Joint Model-Policy Optimization for Model-Based RL. CoRR (2021)
Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jake Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. CoRR (2021)
Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine
Model-Based Visual Planning with Self-Supervised Functional Distances. ICLR (2021)
Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, Ruslan Salakhutdinov
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers. ICLR (2021)
Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jacob Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. ICML (2021)
Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov
Recurrent Model-Free RL is a Strong Baseline for Many POMDPs. CoRR (2021)
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
Robust Predictable Control. CoRR (2021)
Shuby Deshpande, Benjamin Eysenbach, Jeff Schneider
Interactive Visualization for Debugging RL. CoRR (2020)
Benjamin Eysenbach, Swapnil Asawa, Shreyas Chaudhari, Ruslan Salakhutdinov, Sergey Levine
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers. CoRR (2020)
Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine
ViNG: Learning Open-World Navigation with Visual Goals. CoRR (2020)
Tianwei Ni, Harshit S. Sikchi, Yufei Wang, Tejus Gupta, Lisa Lee, Benjamin Eysenbach
f-IRL: Inverse Reinforcement Learning via State Marginal Matching. CoRR (2020)
Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine
Model-Based Visual Planning with Self-Supervised Functional Distances. CoRR (2020)
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
C-Learning: Learning to Achieve Goals via Recursive Classification. CoRR (2020)
Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha, Jie Tan, Chelsea Finn
Learning to be Safe: Deep RL with a Safety Critic. CoRR (2020)
Lisa Lee, Benjamin Eysenbach, Ruslan Salakhutdinov, Shixiang Gu, Chelsea Finn
Weakly-Supervised Reinforcement Learning for Controllable Behavior. CoRR (2020)
Benjamin Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement. CoRR (2020)
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric P. Xing, Sergey Levine, Ruslan Salakhutdinov
Efficient Exploration via State Marginal Matching. CoRR (2019)
Benjamin Eysenbach, Sergey Levine
If MaxEnt RL is the Answer, What is the Question? CoRR (2019)
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine
Diversity is All You Need: Learning Skills without a Reward Function. ICLR (Poster) (2019)
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning. CoRR (2019)
Dibya Ghosh, Abhishek Gupta, Justin Fu, Ashwin Reddy, Coline Devin, Benjamin Eysenbach, Sergey Levine
Learning To Reach Goals Without Reinforcement Learning. CoRR (2019)
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine
Diversity is All You Need: Learning Skills without a Reward Function. CoRR (2018)
Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, Sergey Levine
Unsupervised Meta-Learning for Reinforcement Learning. CoRR (2018)
John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings. ICML (2018)
John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings. CoRR (2018)
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning. ICLR (Poster) (2018)
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning. CoRR (2017)
Benjamin Eysenbach, Carl Vondrick, Antonio Torralba
Who is Mistaken? CoRR (2016)