Fetching the paper…
Reading the bibliography…
Deep reinforcement learning policies, despite their outstanding efficiency in simulated visual control tasks, have shown disappointing ability to generalize across disturbances in the input training images.
Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky · 1992
Earlier work this paper cites.
Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
Earlier work this paper cites.
Protecting against evaluation overfitting in empirical reinforcement learning
Shimon Whiteson, Brian Tanner, Matthew E Taylor, and Peter Stone · 2011
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
Earlier work this paper cites.
Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
J Springenberg, Alexey Dosovitskiy, Thomas Brox, and M Riedmiller · 2015
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
Learning to predict where to look in interactive environments using deep recurrent q-learning
Sajad Mousavi, Michael Schukat, Enda Howley, Ali Borji, and Nasser Mozayani · 2016
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Earlier work this paper cites.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Earlier work this paper cites.
Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
Earlier work this paper cites.
Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Earlier work this paper cites.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian · 2018
Earlier work this paper cites.
Generalization and regularization in dqn
Jesse Farebrother, Marlos C Machado, and Michael Bowling · 2018
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
Earlier work this paper cites.
Visualizing and understanding atari agents
Samuel Greydanus, Anurag Koul, Jonathan Dodge, and Alan Fern · 2018
Earlier work this paper cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
Cited alongside, same era.
Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, and Michael Bowling · 2018
Cited alongside, same era.
Assessing generalization in deep reinforcement learning
Charles Packer, Katelyn Gao, Jernej Kos, Philipp Krähenbühl, Vladlen Koltun, and Dawn Song · 2018
Cited alongside, same era.
RISE: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Cited alongside, same era.
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Denis Yarats, Ilya Kostrikov, and Rob Fergus · 2020
Later among the works it cites.
Learning invariant representations for reinforcement learning without reconstruction
Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine · 2020
Later among the works it cites.
Domain generalization with mixstyle
Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang · 2020
Later among the works it cites.
Dribo: Robust deep reinforcement learning via multi-view information bottleneck
Jiameng Fan and Wenchao Li · 2021
Later among the works it cites.
Secant: Self-expert cloning for zero-shot generalization of visual policies
Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, and Animashree Anandkumar · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Cited alongside, same era.
Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman · 2019
Cited alongside, same era.
A theory of regularized markov decision processes
Matthieu Geist, Bruno Scherrer, and Olivier Pietquin · 2019
Cited alongside, same era.
Generalization in reinforcement learning with selective noise injection and information bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, and Katja Hofmann · 2019
Cited alongside, same era.
Obstacle tower: A generalization challenge in vision
Arthur Juliani, Ahmed Khalifa, Vincent-Pierre Berges, Jonathan Harper, Hunter Henry, Adam Crespi, Julian Togelius, and Danny Lange · 2019
Cited alongside, same era.
Observational overfitting in reinforcement learning
Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, and Behnam Neyshabur · 2019
Cited alongside, same era.
Investigating generalisation in continuous deep reinforcement learning
Chenyang Zhao, Olivier Sigaud, Freek Stulp, and Timothy M Hospedales · 2019
Cited alongside, same era.
Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis
Thomas Fel, Rémi Cadène, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, and Thomas Serre · 2021
Later among the works it cites.
Learning task informed abstractions
Xiang Fu, Ge Yang, Pulkit Agrawal, and Tommi Jaakkola · 2021
Later among the works it cites.
Generalization in reinforcement learning by soft data augmentation
Nicklas Hansen and Xiaolong Wang · 2021
Later among the works it cites.
Stabilizing deep q-learning with convnets and vision transformers under data augmentation
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2021
Later among the works it cites.
Improving deep learning interpretability by saliency guided training
Aya Abdelsalam Ismail, Hector Corrada Bravo, and Soheil Feizi · 2021
Later among the works it cites.
Prioritized level replay
Minqi Jiang, Edward Grefenstette, and Tim Rocktäschel · 2021
Later among the works it cites.
Decoupling value and policy for generalization in reinforcement learning
Roberta Raileanu and Rob Fergus · 2021
Later among the works it cites.
Automatic data augmentation for generalization in reinforcement learning
Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, and Rob Fergus · 2021
Later among the works it cites.
The distracting control suite–a challenging benchmark for reinforcement learning from pixels
Austin Stone, Oscar Ramirez, Kurt Konolige, and Rico Jonschkowski · 2021
Later among the works it cites.
Unsupervised visual attention and invariance for reinforcement learning
Xudong Wang, Long Lian, and Stella X Yu · 2021
Later among the works it cites.
Local feature swapping for generalization in reinforcement learning
David Bertoin and Emmanuel Rachelson · 2022
Closest in time.
Look closer: Bridging egocentric and third-person views with transformers for robotic manipulation
Rishabh Jangir, Nicklas Hansen, Sambaran Ghosal, Mohit Jain, and Xiaolong Wang · 2022
Closest in time.
Cross-trajectory representation learning for zero-shot generalization in RL
Bogdan Mazoure, Ahmed M Ahmed, R Devon Hjelm, Andrey Kolobov, and Patrick MacAlpine · 2022
Closest in time.
Don’t touch what matters: Task-aware lipschitz data augmentationfor visual reinforcement learning
Zhecheng Yuan, Guozheng Ma, Yao Mu, Bo Xia, Bo Yuan, Xueqian Wang, Ping Luo, and Huazhe Xu · 2022
Closest in time.