Learning Representations for Pixel-based Control: What Matters and Why?

15 November 2021

Papers citing "Learning Representations for Pixel-based Control: What Matters and Why?"

7 / 7 papers shown

Title
Uncovering RL Integration in SSL Loss: Objective-Specific Implications for Data-Efficient RL Ömer Veysel Çağatan Barış Akgün OffRL 44 0 0 22 Oct 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 AI4TS AI4CE 29 21 0 17 Jan 2024
Distributionally Robust Statistical Verification with Imprecise Neural Networks Souradeep Dutta Michele Caprio Vivian Lin Matthew Cleaveland Kuk Jin Jang I. Ruchkin O. Sokolsky Insup Lee OOD AAML 51 7 0 28 Aug 2023
A Mixture of Surprises for Unsupervised Reinforcement Learning Andrew Zhao Matthieu Lin Yangguang Li Yue Liu Gao Huang 28 13 0 13 Oct 2022
A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning Guozheng Ma Zhen Wang Zhecheng Yuan Xueqian Wang Bo Yuan Dacheng Tao OffRL 47 27 0 10 Oct 2022
The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels Austin Stone Oscar Ramirez K. Konolige Rico Jonschkowski 137 101 0 07 Jan 2021
Decoupling Representation Learning from Reinforcement Learning Adam Stooke Kimin Lee Pieter Abbeel Michael Laskin SSL DRL 288 341 0 14 Sep 2020