39 citations · 281 across the 47 of their papers we have counts for
8 papers · 2 filters
Model-Based Visual Planning with Self-Supervised Functional Distances
Stephen Tian, Suraj Nair, Frederik Ebert +4
A generalist robot must be able to complete a variety of tasks in its environment. One appealing way to specify each task is in terms of a goal observation. However, learning goal-…
f-IRL: Inverse Reinforcement Learning via State Marginal Matching
Tianwei Ni, Harshit Sikchi, Yufei Wang +3
Imitation learning is well-suited for robotic tasks where it is difficult to directly program the behavior or specify a cost for optimal control. In this work, we propose a method…
C-Learning: Learning to Achieve Goals via Recursive Classification
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
We study the problem of predicting and controlling the future state distribution of an autonomous agent. This problem, which can be viewed as a reframing of goal-conditioned reinfo…
Learning to be Safe: Deep RL with a Safety Critic
Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha +2
Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself. A natural first a…
Interactive Visualization for Debugging RL
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. While reinforcement learning…
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
Benjamin Eysenbach, Swapnil Asawa, Shreyas Chaudhari +2
We propose a simple, practical, and intuitive approach for domain adaptation in reinforcement learning. Our approach stems from the idea that the agent's experience in the source d…