Fetching the paper…
Reading the bibliography…
While reinforcement learning algorithms can learn effective policies for complex tasks, these policies are often brittle to even minor task variations, especially when variations are not explicitly provided during training.
Kemin Zhou and John Comstock Doyle · 1998
Earlier work this paper cites.
Robust control of markov decision processes with uncertain transition matrices
Arnab Nilim and Laurent El Ghaoui · 2005
Earlier work this paper cites.
Policy gradients with parameter-based exploration for control
Frank Sehnke, Christian Osendorfer, Thomas Rückstieß, Alex Graves, Jan Peters, and Jürgen Schmidhuber · 2008
Earlier work this paper cites.
Percentile optimization for markov decision processes with parameter uncertainty
Erick Delage and Shie Mannor · 2010
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
Robust markov decision processes
Wolfram Wiesemann, Daniel Kuhn, and Berç Rustem · 2013
Earlier work this paper cites.
Risk-sensitive and robust decision-making: a cvar optimization approach
Yinlam Chow, Aviv Tamar, Shie Mannor, and Marco Pavone · 2015
Earlier work this paper cites.
Rl 2 : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2016
Earlier work this paper cites.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Earlier work this paper cites.
Epopt: Learning robust neural network policies using model ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, and Sergey Levine · 2016
Earlier work this paper cites.
Cad2rl: Real single-image flight without a single real image
Fereshteh Sadeghi and Sergey Levine · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
Earlier work this paper cites.
Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
Earlier work this paper cites.
Uncertainty-aware reinforcement learning for collision avoidance
Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Infogail: Interpretable imitation learning from visual demonstrations
Yunzhu Li, Jiaming Song, and Stefano Ermon · 2017
Cited alongside, same era.
Tactics of adversarial attack on deep reinforcement learning agents
Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, and Min Sun · 2017
Cited alongside, same era.
Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
Cited alongside, same era.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Cited alongside, same era.
Robust deep reinforcement learning with adversarial attacks
Anay Pattanaik, Zhenyi Tang, Shuijing Liu, Gautham Bommannan, and Girish Chowdhary · 2018
Later among the works it cites.
Reinforcement learning with perturbed rewards
Jingkang Wang, Yang Liu, and Bo Li · 2018
Later among the works it cites.
A study on overfitting in deep reinforcement learning
Chiyuan Zhang, Oriol Vinyals, Remi Munos, and Samy Bengio · 2018
Later among the works it cites.
Quantile qt-opt for risk-aware vision-based robotic grasping
Cristian Bodnar, Adrian Li, Karol Hausman, Peter Pastor, and Mrinal Kalakrishnan · 2019
Later among the works it cites.
Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, and Alexander J Smola · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Preparing for the unknown: Learning a universal policy with online system identification
Wenhao Yu, Jie Tan, C Karen Liu, and Greg Turk · 2017
Cited alongside, same era.
Vfunc: a deep generative model for functions
Philip Bachman, Riashat Islam, Alessandro Sordoni, and Zafarali Ahmed · 2018
Cited alongside, same era.
Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman · 2018
Cited alongside, same era.
Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
Cited alongside, same era.
Generalization and regularization in dqn
Jesse Farebrother, Marlos C Machado, and Michael Bowling · 2018
Cited alongside, same era.
Unsupervised meta-learning for reinforcement learning
Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, and Sergey Levine · 2018
Cited alongside, same era.
Later among the works it cites.
Adversarial policies: Attacking deep reinforcement learning
Adam Gleave, Michael Dennis, Neel Kant, Cody Wild, Sergey Levine, and Stuart Russell · 2019
Later among the works it cites.
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
Later among the works it cites.
Unsupervised curricula for visual meta-reinforcement learning
Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, and Chelsea Finn · 2019
Later among the works it cites.
Improving generalization in meta reinforcement learning using learned objectives
Louis Kirsch, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2019
Later among the works it cites.
Robust multi-agent reinforcement learning via minimax deep deterministic policy gradient
Shihui Li, Yi Wu, Xinyue Cui, Honghua Dong, Fei Fang, and Stuart Russell · 2019
Later among the works it cites.
Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2019
Later among the works it cites.
Risk averse robust adversarial reinforcement learning
Xinlei Pan, Daniel Seita, Yang Gao, and John Canny · 2019
Later among the works it cites.
Dynamics-aware unsupervised discovery of skills
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2019
Later among the works it cites.
Yichuan Charlie Tang, Jian Zhang, and Ruslan Salakhutdinov · 2019
Later among the works it cites.
Action robust reinforcement learning and applications in continuous control
Chen Tessler, Yonathan Efroni, and Shie Mannor · 2019
Later among the works it cites.