Who is Mistaken? - Benjamin Eysenbach; Carl Vondrick; Antonio Torralba 001 (CoRR, 2016)

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning. - Benjamin Eysenbach; Shixiang Gu; Julian Ibarz; Sergey Levine (ICLR (Poster), 2018)

Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings. - John D. Co-Reyes; Yuxuan Liu 001; Abhishek Gupta 004; Benjamin Eysenbach; Pieter Abbeel; Sergey Levine (ICML, 2018)

Unsupervised Meta-Learning for Reinforcement Learning. - Abhishek Gupta 004; Benjamin Eysenbach; Chelsea Finn; Sergey Levine (CoRR, 2018)

Clustervision: Visual Supervision of Unsupervised Clustering. - Bum Chul Kwon; Ben Eysenbach; Janu Verma; Kenney Ng; Christopher deFilippi; Walter F. Stewart; Adam Perer (IEEE Trans. Vis. Comput. Graph., 2018)

Diversity is All You Need: Learning Skills without a Reward Function. - Benjamin Eysenbach; Abhishek Gupta 004; Julian Ibarz; Sergey Levine (ICLR (Poster), 2019)

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning. - Ben Eysenbach; Ruslan Salakhutdinov; Sergey Levine (NeurIPS, 2019)

Unsupervised Curricula for Visual Meta-Reinforcement Learning. - Allan Jabri; Kyle Hsu; Abhishek Gupta 004; Ben Eysenbach; Sergey Levine; Chelsea Finn (NeurIPS, 2019)

Efficient Exploration via State Marginal Matching. - Lisa Lee; Benjamin Eysenbach; Emilio Parisotto; Eric P. Xing; Sergey Levine; Ruslan Salakhutdinov (CoRR, 2019)

If MaxEnt RL is the Answer, What is the Question? - Benjamin Eysenbach; Sergey Levine (CoRR, 2019)

Learning To Reach Goals Without Reinforcement Learning. - Dibya Ghosh; Abhishek Gupta 004; Justin Fu; Ashwin Reddy; Coline Devin; Benjamin Eysenbach; Sergey Levine (CoRR, 2019)

f-IRL: Inverse Reinforcement Learning via State Marginal Matching. - Tianwei Ni; Harshit S. Sikchi; Yufei Wang; Tejus Gupta; Lisa Lee; Ben Eysenbach (CoRL, 2020)

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement. - Ben Eysenbach; Xinyang Geng; Sergey Levine; Ruslan Salakhutdinov (NeurIPS, 2020)

Weakly-Supervised Reinforcement Learning for Controllable Behavior. - Lisa Lee; Ben Eysenbach; Ruslan Salakhutdinov; Shixiang Shane Gu; Chelsea Finn (NeurIPS, 2020)

Interactive Visualization for Debugging RL. - Shuby Deshpande; Benjamin Eysenbach; Jeff Schneider (CoRR, 2020)

Learning to be Safe: Deep RL with a Safety Critic. - Krishnan Srinivasan; Benjamin Eysenbach; Sehoon Ha; Jie Tan 001; Chelsea Finn (CoRR, 2020)

Rapid Exploration for Open-World Navigation with Latent Goal Models. - Dhruv Shah; Benjamin Eysenbach; Nicholas Rhinehart; Sergey Levine (CoRL, 2021)

Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers. - Benjamin Eysenbach; Shreyas Chaudhari; Swapnil Asawa; Sergey Levine; Ruslan Salakhutdinov (ICLR, 2021)

C-Learning: Learning to Achieve Goals via Recursive Classification. - Benjamin Eysenbach; Ruslan Salakhutdinov; Sergey Levine (ICLR, 2021)

Learning to Reach Goals via Iterated Supervised Learning. - Dibya Ghosh; Abhishek Gupta 004; Ashwin Reddy; Justin Fu; Coline Manon Devin; Benjamin Eysenbach; Sergey Levine (ICLR, 2021)

Model-Based Visual Planning with Self-Supervised Functional Distances. - Stephen Tian; Suraj Nair 003; Frederik Ebert; Sudeep Dasari; Benjamin Eysenbach; Chelsea Finn; Sergey Levine (ICLR, 2021)

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. - Yevgen Chebotar; Karol Hausman; Yao Lu 006; Ted Xiao; Dmitry Kalashnikov; Jacob Varley; Alex Irpan; Benjamin Eysenbach; Ryan Julian; Chelsea Finn; Sergey Levine (ICML, 2021)

ViNG: Learning Open-World Navigation with Visual Goals. - Dhruv Shah; Benjamin Eysenbach; Gregory Kahn; Nicholas Rhinehart; Sergey Levine (ICRA, 2021)

Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification. - Ben Eysenbach; Sergey Levine; Ruslan Salakhutdinov (NeurIPS, 2021)

Robust Predictable Control. - Ben Eysenbach; Ruslan Salakhutdinov; Sergey Levine (NeurIPS, 2021)

RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models. - Dhruv Shah; Benjamin Eysenbach; Nicholas Rhinehart; Sergey Levine (CoRR, 2021)

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. - Yevgen Chebotar; Karol Hausman; Yao Lu 006; Ted Xiao; Dmitry Kalashnikov; Jake Varley; Alex Irpan; Benjamin Eysenbach; Ryan Julian; Chelsea Finn; Sergey Levine (CoRR, 2021)

Recurrent Model-Free RL is a Strong Baseline for Many POMDPs. - Tianwei Ni; Benjamin Eysenbach; Ruslan Salakhutdinov (CoRR, 2021)

RvS: What is Essential for Offline RL via Supervised Learning? - Scott Emmons; Benjamin Eysenbach; Ilya Kostrikov; Sergey Levine (ICLR, 2022)

Maximum Entropy RL (Provably) Solves Some Robust RL Problems. - Benjamin Eysenbach; Sergey Levine (ICLR, 2022)

The Information Geometry of Unsupervised Reinforcement Learning. - Benjamin Eysenbach; Ruslan Salakhutdinov; Sergey Levine (ICLR, 2022)

C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks. - Tianjun Zhang; Benjamin Eysenbach; Ruslan Salakhutdinov; Sergey Levine; Joseph E. Gonzalez (ICLR, 2022)

Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs. - Tianwei Ni; Benjamin Eysenbach; Ruslan Salakhutdinov (ICML, 2022)

Mismatched No More: Joint Model-Policy Optimization for Model-Based RL. - Benjamin Eysenbach; Alexander Khazatsky; Sergey Levine; Ruslan Salakhutdinov (NeurIPS, 2022)

Imitating Past Successes can be Very Suboptimal. - Benjamin Eysenbach; Soumith Udatha; Russ Salakhutdinov; Sergey Levine (NeurIPS, 2022)

Contrastive Learning as Goal-Conditioned Reinforcement Learning. - Benjamin Eysenbach; Tianjun Zhang; Sergey Levine; Ruslan Salakhutdinov (NeurIPS, 2022)

Learning Options via Compression. - Yiding Jiang; Evan Zheran Liu; Benjamin Eysenbach; J. Zico Kolter; Chelsea Finn (NeurIPS, 2022)

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions. - Amrith Setlur; Benjamin Eysenbach; Virginia Smith; Sergey Levine (NeurIPS, 2022)

Contrastive Value Learning: Implicit Models for Simple Offline RL. - Bogdan Mazoure; Benjamin Eysenbach; Ofir Nachum; Jonathan Tompson (CoRL, 2023)

Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective. - Raj Ghugare; Homanga Bharadhwaj; Benjamin Eysenbach; Sergey Levine; Russ Salakhutdinov (ICLR, 2023)

Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. - Amrith Setlur; Don Kurian Dennis; Benjamin Eysenbach; Aditi Raghunathan; Chelsea Finn; Virginia Smith; Sergey Levine (ICLR, 2023)

A Connection between One-Step RL and Critic Regularization in Reinforcement Learning. - Benjamin Eysenbach; Matthieu Geist; Sergey Levine; Ruslan Salakhutdinov (ICML, 2023)

Contrastive Example-Based Control. - Kyle Beltran Hatch; Benjamin Eysenbach; Rafael Rafailov; Tianhe Yu; Ruslan Salakhutdinov; Sergey Levine; Chelsea Finn (L4DC, 2023)

When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment. - Tianwei Ni; Michel Ma; Benjamin Eysenbach; Pierre-Luc Bacon (NeurIPS, 2023)

HIQL: Offline Goal-Conditioned RL with Latent States as Actions. - Seohong Park; Dibya Ghosh; Benjamin Eysenbach; Sergey Levine (NeurIPS, 2023)

Stabilizing Contrastive RL: Techniques for Offline Goal Reaching. - Chongyi Zheng; Benjamin Eysenbach; Homer Walke; Patrick Yin; Kuan Fang; Ruslan Salakhutdinov; Sergey Levine (CoRR, 2023)

A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning. - Benjamin Eysenbach; Matthieu Geist; Sergey Levine; Ruslan Salakhutdinov (CoRR, 2023)

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes and Inferring How to Get There - Benjamin Eysenbach (2023)

Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View. - Raj Ghugare; Matthieu Geist; Glen Berseth; Benjamin Eysenbach (ICLR, 2024)

Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations. - Yongyuan Liang; Yanchao Sun; Ruijie Zheng; Xiangyu Liu; Benjamin Eysenbach; Tuomas Sandholm; Furong Huang; Stephen Marcus McAleer (ICLR, 2024)

Bridging State and History Representations: Understanding Self-Predictive RL. - Tianwei Ni; Benjamin Eysenbach; Erfan Seyedsalehi; Michel Ma; Clement Gehring; Aditya Mahajan; Pierre-Luc Bacon (ICLR, 2024)

Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data. - Chongyi Zheng; Benjamin Eysenbach; Homer Rich Walke; Patrick Yin; Kuan Fang; Ruslan Salakhutdinov; Sergey Levine (ICLR, 2024)

Contrastive Difference Predictive Coding. - Chongyi Zheng; Ruslan Salakhutdinov; Benjamin Eysenbach (ICLR, 2024)

A Rate-Distortion View of Uncertainty Quantification. - Ifigeneia Apostolopoulou; Benjamin Eysenbach; Frank Nielsen; Artur Dubrawski (ICML, 2024)

Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making. - Vivek Myers; Chongyi Zheng; Anca D. Dragan; Sergey Levine; Benjamin Eysenbach (ICML, 2024)

Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference. - Benjamin Eysenbach; Vivek Myers; Ruslan Salakhutdinov; Sergey Levine (NeurIPS, 2024)

Learning to Assist Humans without Inferring Rewards. - Vivek Myers; Evan Ellis; Sergey Levine; Benjamin Eysenbach; Anca D. Dragan (NeurIPS, 2024)

Accelerating Goal-Conditioned RL Algorithms and Research. - Michal Bortkiewicz; Wladek Palucki; Vivek Myers; Tadeusz Dziarmaga; Tomasz Arczewski; Lukasz Kucinski; Benjamin Eysenbach (CoRR, 2024)

GHIL-Glue: Hierarchical Control with Filtered Subgoal Images. - Kyle Beltran Hatch; Ashwin Balakrishna; Oier Mees; Suraj Nair 003; Seohong Park; Blake Wulfe; Masha Itkina; Benjamin Eysenbach; Sergey Levine; Thomas Kollar; Benjamin Burchfiel (CoRR, 2024)

Accelerating Goal-Conditioned Reinforcement Learning Algorithms and Research. - Michal Bortkiewicz; Wladyslaw Palucki; Vivek Myers; Tadeusz Dziarmaga; Tomasz Arczewski; Lukasz Kucinski; Benjamin Eysenbach (ICLR, 2025)

The "Law" of the Unconscious Contrastive Learner: Probabilistic Alignment of Unpaired Modalities. - Yongwei Che; Benjamin Eysenbach (ICLR, 2025)

A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals. - Grace Liu; Michael Tang; Benjamin Eysenbach (ICLR, 2025)

Horizon Generalization in Reinforcement Learning. - Vivek Myers; Catherine Ji; Benjamin Eysenbach (ICLR, 2025)

OGBench: Benchmarking Offline Goal-Conditioned RL. - Seohong Park; Kevin Frans; Benjamin Eysenbach; Sergey Levine (ICLR, 2025)

Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning. - Chongyi Zheng; Jens Tuyls; Joanne Peng; Benjamin Eysenbach (ICLR, 2025)

1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities. - Kevin Wang; Ishaan Javali; Michal Bortkiewicz; Tomasz Trzcinski; Benjamin Eysenbach (CoRR, 2025)

Normalizing Flows are Capable Models for RL. - Raj Ghugare; Benjamin Eysenbach (CoRR, 2025)

Horizon Reduction Makes RL Scalable. - Seohong Park; Kevin Frans; Deepinder Mann; Benjamin Eysenbach; Aviral Kumar; Sergey Levine (CoRR, 2025)

Intention-Conditioned Flow Occupancy Models. - Chongyi Zheng; Seohong Park; Sergey Levine; Benjamin Eysenbach (CoRR, 2025)

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning. - Patrik Reizinger; Bálint Mucsányi; Siyuan Guo; Benjamin Eysenbach; Bernhard Schölkopf; Wieland Brendel (CoRR, 2025)