1 code implementation • 24 Jun 2024 • Vivek Myers, Chongyi Zheng, Anca Dragan, Sergey Levine, Benjamin Eysenbach

In this paper, we build on prior work in contrastive learning and quasimetrics to show how successor features learned by contrastive learning (after a change of variables) form a temporal distance that does satisfy the triangle inequality, even in stochastic settings.

1 code implementation • 16 Jun 2024 • Ifigeneia Apostolopoulou, Benjamin Eysenbach, Frank Nielsen, Artur Dubrawski

In this paper, we introduce Distance Aware Bottleneck (DAB), i. e., a new method for enriching deep neural networks with this property.

1 code implementation • 6 Mar 2024 • Benjamin Eysenbach, Vivek Myers, Ruslan Salakhutdinov, Sergey Levine

The key idea is to apply a variant of contrastive learning to time series data.

1 code implementation • 20 Jan 2024 • Raj Ghugare, Matthieu Geist, Glen Berseth, Benjamin Eysenbach

Based on this analysis, we construct new datasets to explicitly test for this property, revealing that SL-based methods lack this stitching property and hence fail to perform combinatorial generalization.

1 code implementation • 17 Jan 2024 • Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon

These findings culminate in a set of preliminary guidelines for RL practitioners.

1 code implementation • 31 Oct 2023 • Chongyi Zheng, Ruslan Salakhutdinov, Benjamin Eysenbach

Predicting and reasoning about the future lie at the heart of many time-series questions.

1 code implementation • 24 Jul 2023 • Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn

In this paper, we propose a method for offline, example-based control that learns an implicit model of multi-step transitions, rather than a reward function.

1 code implementation • 24 Jul 2023 • Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov

One-step methods perform regularization by doing just a single step of policy improvement, while critic regularization methods do many steps of policy improvement with a regularized objective.

no code implementations • 22 Jul 2023 • Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Xiangyu Liu, Benjamin Eysenbach, Tuomas Sandholm, Furong Huang, Stephen Mcaleer

To tackle this challenge, we propose GRAD, a novel game-theoretic approach that treats the temporally-coupled robust RL problem as a partially observable two-player zero-sum game.

1 code implementation • NeurIPS 2023 • Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine

This structure can be very useful, as assessing the quality of actions for nearby goals is typically easier than for more distant goals.

2 code implementations • NeurIPS 2023 • Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon

The Transformer architecture has been very successful to solve problems that involve long-term dependencies, including in the RL domain.

1 code implementation • 6 Jun 2023 • Chongyi Zheng, Benjamin Eysenbach, Homer Walke, Patrick Yin, Kuan Fang, Ruslan Salakhutdinov, Sergey Levine

Robotic systems that rely primarily on self-supervised learning have the potential to decrease the amount of human annotation and engineering effort required to learn control strategies.

1 code implementation • 6 Feb 2023 • Amrith Setlur, Don Dennis, Benjamin Eysenbach, aditi raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine

Some robust training algorithms (e. g., Group DRO) specialize to group shifts and require group information on all training points.

1 code implementation • 8 Dec 2022 • Yiding Jiang, Evan Zheran Liu, Benjamin Eysenbach, Zico Kolter, Chelsea Finn

Identifying statistical regularities in solutions to some tasks in multi-task reinforcement learning can accelerate the learning of new tasks.

no code implementations • 3 Nov 2022 • Bogdan Mazoure, Benjamin Eysenbach, Ofir Nachum, Jonathan Tompson

In this paper, we propose Contrastive Value Learning (CVL), which learns an implicit, multi-step model of the environment dynamics.

no code implementations • 18 Sep 2022 • Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov

In this work, we propose a single objective which jointly optimizes a latent-space model and policy to achieve high returns while remaining self-consistent.

no code implementations • 15 Jun 2022 • Benjamin Eysenbach, Tianjun Zhang, Ruslan Salakhutdinov, Sergey Levine

While deep RL should automatically acquire such good representations, prior work often finds that learning representations in an end-to-end fashion is unstable and instead equip RL algorithms with additional representation learning parts (e. g., auxiliary losses, data augmentation).

no code implementations • 7 Jun 2022 • Benjamin Eysenbach, Soumith Udatha, Sergey Levine, Ruslan Salakhutdinov

Prior work has proposed a simple strategy for reinforcement learning (RL): label experience with the outcomes achieved in that experience, and then imitate the relabeled experience.

no code implementations • 3 Jun 2022 • Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine

Supervised learning methods trained with maximum likelihood objectives often overfit on training data.

1 code implementation • 20 Dec 2021 • Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine

Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL.

no code implementations • ICLR 2022 • Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez

Goal-conditioned reinforcement learning (RL) can solve tasks in a wide range of domains, including navigation and manipulation, but learning to reach distant goals remains a central challenge to the field.

2 code implementations • 11 Oct 2021 • Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov

However, prior work has found that such recurrent model-free RL methods tend to perform worse than more specialized algorithms that are designed for specific types of POMDPs.

1 code implementation • ICLR 2022 • Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

In this work, we show that unsupervised skill discovery algorithms based on mutual information maximization do not learn skills that are optimal for every possible reward function.

1 code implementation • 6 Oct 2021 • Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov

Many model-based reinforcement learning (RL) methods follow a similar template: fit a model to previously observed data, and then use data from that model for RL or planning.

Model-based Reinforcement Learning Reinforcement Learning (RL)

no code implementations • ICLR 2022 • Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine

These methods, which we collectively refer to as reinforcement learning via supervised learning (RvS), involve a number of design decisions, such as policy architectures and how the conditioning variable is constructed.

1 code implementation • NeurIPS 2021 • Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Many of the challenges facing today's reinforcement learning (RL) algorithms, such as robustness, generalization, transfer, and computational efficiency are closely related to compression.

no code implementations • 15 Apr 2021 • Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jake Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine

We consider the problem of learning useful robotic skills from previously collected offline data without access to manually specified rewards or additional online exploration, a setting that is becoming increasingly important for scaling robot learning by reusing past robotic data.

no code implementations • 12 Apr 2021 • Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine

We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments.

1 code implementation • NeurIPS 2021 • Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov

Can we devise RL algorithms that instead enable users to specify tasks simply by providing examples of successful outcomes?

no code implementations • ICLR 2022 • Benjamin Eysenbach, Sergey Levine

Many potential applications of reinforcement learning (RL) require guarantees that the agent will perform well in the face of disturbances to the dynamics or reward function.

1 code implementation • ICLR 2021 • Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine

In our experiments, we find that our method can successfully learn models that perform a variety of tasks at test-time, moving objects amid distractors with a simulated robotic arm and even learning to open and close a drawer using a real-world robot.

no code implementations • 17 Dec 2020 • Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine

We propose a learning-based navigation system for reaching visually indicated goals and demonstrate this system on a real mobile robot platform.

no code implementations • ICLR 2021 • Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

This problem, which can be viewed as a reframing of goal-conditioned reinforcement learning (RL), is centered around learning a conditional probability density function over future states.

1 code implementation • 9 Nov 2020 • Tianwei Ni, Harshit Sikchi, YuFei Wang, Tejus Gupta, Lisa Lee, Benjamin Eysenbach

Our method outperforms adversarial imitation learning methods in terms of sample efficiency and the required number of expert trajectories on IRL benchmarks.

no code implementations • 27 Oct 2020 • Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha, Jie Tan, Chelsea Finn

Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself.

no code implementations • 14 Aug 2020 • Shuby Deshpande, Benjamin Eysenbach, Jeff Schneider

Visualization tools for supervised learning allow users to interpret, introspect, and gain an intuition for the successes and failures of their models.

1 code implementation • ICLR 2021 • Benjamin Eysenbach, Swapnil Asawa, Shreyas Chaudhari, Sergey Levine, Ruslan Salakhutdinov

Building off of a probabilistic view of RL, we formally show that we can achieve this goal by compensating for the difference in dynamics by modifying the reward function.

no code implementations • NeurIPS 2020 • Lisa Lee, Benjamin Eysenbach, Ruslan Salakhutdinov, Shixiang Shane Gu, Chelsea Finn

Reinforcement learning (RL) is a powerful framework for learning to take actions to solve tasks.

1 code implementation • NeurIPS 2020 • Benjamin Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov

In this paper, we show that hindsight relabeling is inverse RL, an observation that suggests that we can use inverse RL in tandem for RL algorithms to efficiently solve many tasks.

2 code implementations • ICLR 2021 • Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Devin, Benjamin Eysenbach, Sergey Levine

Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards.

Multi-Goal Reinforcement Learning Reinforcement Learning (RL)

no code implementations • 4 Oct 2019 • Benjamin Eysenbach, Sergey Levine

In particular, we show (1) that MaxEnt RL can be used to solve a certain class of POMDPs, and (2) that MaxEnt RL is equivalent to a two-player game where an adversary chooses the reward function.

no code implementations • 25 Sep 2019 • Dibya Ghosh, Abhishek Gupta, Justin Fu, Ashwin Reddy, Coline Devin, Benjamin Eysenbach, Sergey Levine

By maximizing the likelihood of good actions provided by an expert demonstrator, supervised imitation learning can produce effective policies without the algorithmic complexities and optimization challenges of reinforcement learning, at the cost of requiring an expert demonstrator -- typically a person -- to provide the demonstrations.

no code implementations • 25 Sep 2019 • Dhruv Ramani, Benjamin Eysenbach

Our imaginative module can be seen as a ``plug-and-play'' approach to ensuring safety, as it is compatible with any existing RL algorithm and any task with discrete action space.

1 code implementation • 12 Jun 2019 • Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, Ruslan Salakhutdinov

The SMM objective can be viewed as a two-player, zero-sum game between a state density model and a parametric policy, an idea that we use to build an algorithm for optimizing the SMM objective.

1 code implementation • NeurIPS 2019 • Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

We introduce a general control algorithm that combines the strengths of planning and reinforcement learning to effectively solve these tasks.

no code implementations • ICLR 2020 • Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, Sergey Levine

In the context of reinforcement learning, meta-learning algorithms acquire reinforcement learning procedures to solve new problems more efficiently by utilizing experience from prior tasks.

no code implementations • ICML 2018 • John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine

We show that we can learn continuous latent representations of trajectories, which are effective in solving temporally extended and multi-stage problems.

Hierarchical Reinforcement Learning
reinforcement-learning
**+2**

3 code implementations • ICLR 2019 • Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine

On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping.

1 code implementation • ICLR 2018 • Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine

In this work, we propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and reset policy, with the reset policy resetting the environment for a subsequent attempt.

no code implementations • 4 Dec 2016 • Benjamin Eysenbach, Carl Vondrick, Antonio Torralba

We then create a representation of characters' beliefs for two tasks in human action understanding: predicting who is mistaken, and when they are mistaken.

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