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Contrastive Value Learning: Implicit Models for Simple Offline RL


Nov 03, 2022
Bogdan Mazoure, Benjamin Eysenbach, Ofir Nachum, Jonathan Tompson

* Deep Reinforcement Learning Workshop, NeurIPS 2022 

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Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective


Sep 18, 2022
Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov

* 9 pages (without references and appendix), 17 figures, 25 Pages (total), Project website with code: \url{https://alignedlatentmodels.github.io/

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Contrastive Learning as Goal-Conditioned Reinforcement Learning


Jun 15, 2022
Benjamin Eysenbach, Tianjun Zhang, Ruslan Salakhutdinov, Sergey Levine

* Code is available on the website: https://ben-eysenbach.github.io/contrastive_rl 

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Imitating Past Successes can be Very Suboptimal


Jun 07, 2022
Benjamin Eysenbach, Soumith Udatha, Sergey Levine, Ruslan Salakhutdinov


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Adversarial Unlearning: Reducing Confidence Along Adversarial Directions


Jun 03, 2022
Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine


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


Dec 20, 2021
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine


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


Oct 22, 2021
Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez


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


Oct 11, 2021
Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov


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


Oct 06, 2021
Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov


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The Information Geometry of Unsupervised Reinforcement Learning


Oct 06, 2021
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine


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