Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations

Human-level control through deep reinforcement learning

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Mastering the game of Go with deep neural networks and tree search

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Continuous control with deep reinforcement learning

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Explaining and Harnessing Adversarial Examples

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Intriguing properties of neural networks

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Adversarial examples in the physical world

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Curriculum learning

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DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

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MuJoCo: A physics engine for model-based control

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Trust region policy optimization

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Reinforcement learning in robotics: A survey

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Universal Adversarial Perturbations

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End-to-end training of deep visuomotor policies

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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection

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Evasion attacks against machine learning at test time

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Target-driven visual navigation in indoor scenes using deep reinforcement learning

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Transfer Learning for Reinforcement Learning Domains: A Survey

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Linear robust control

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Progressive Neural Networks

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Untitled work

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Evasion Attacks against Machine Learning at Test Time

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Can machine learning be secure?

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Robust Control of Markov Decision Processes with Uncertain Transition Matrices

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On the (Statistical) Detection of Adversarial ExamplesPreprint

Grosse, K., Manoharan, P., Papernot, N., Michael Backes, A., & McDaniel, P. D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.06280 · Cited by 546 · see citing works

Sim-to-Real Robot Learning from Pixels with Progressive Nets

Rusu, A. A., Vecerik, M., Rothörl, T., Heess, N., Pascanu, R., & Hadsell, R. (2016) · arXiv · doi:10.48550/arxiv.1610.04286 · Cited by 385 · see citing works

Learning modular neural network policies for multi-task and multi-robot transfer

Devin, C., Gupta, A., Darrell, T., Abbeel, P., & Levine, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989250 · Cited by 324 · see citing works

Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples.Preprint

Papernot, N., McDaniel, P. D., Goodfellow, I., Jha, S., Celik, Z. B., & Swami, A. (2016) · arXiv (Cornell University) · Cited by 286 · see citing works

Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks

Behzadan, V., & Munir, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-62416-7_19 · Cited by 247 · see citing works

Preparing for the unknown: Learning a universal policy with online system identification

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Empagliflozin for Type 2 Diabetes Mellitus: An Overview of Phase 3 Clinical Trials

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Using inaccurate models in reinforcement learning

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EPOpt: Learning Robust Neural Network Policies Using Model EnsemblesPreprint

Rajeswaran, A., Ghotra, S., Ravindran, B., & Levine, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.01283 · Cited by 185 · see citing works

Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning

Thananjeyan, B., Garg, A., Krishnan, S., Chen, C., Miller, L., & Goldberg, K. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989275 · Cited by 144 · see citing works

AdaPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems

Harrison, J., Garg, A., Ivanovic, B., Zhu, Y., Savarese, S., Fei-Fei, L., & Pavone, M. (2019) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-28619-4_34 · Cited by 21 · see citing works

ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems

Harrison, J., Garg, A., Ivanovic, B., Zhu, Y., Savarese, S., Fei-Fei, L., & Pavone, M. (2017) · arXiv · doi:10.48550/arxiv.1707.04674 · Cited by 21

Loss bounds for uncertain transition probabilities in Markov decision processes

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On the distance to uncontrollability and the distance to instability and their relation to some condition numbers in control

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DeepMask: Masking DNN Models for robustness against adversarial samples.Preprint

Gao, J., Wang, B., & Qi, Y. (2017) · arXiv (Cornell University) · Cited by 8 · see citing works

Uncontrollability for Linear Autonomous Multi-input Dynamical Systems Depending on Parameters

Mailybaev, A. A. (2003) · SIAM Journal on Control and Optimization · doi:10.1137/s0363012902405339 · Cited by 7 · see citing works

The Distance to Uncontrollability via Linear Matrix Inequalities

Boyce, S. (2010) · VTechWorks (Virginia Tech) · Cited by 1 · see citing works

Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning

Salvato, E., Fenu, G., Medvet, E., & Pellegrino, F. A. (2021) · IEEE Access · doi:10.1109/access.2021.3126658 · Cited by 200 · see citing works

ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems

Harrison, J., Garg, A., Ivanovic, B., Zhu, Y., Savarese, S., Fei-Fei, L., & Pavone, M. (2017) · arXiv · doi:10.48550/arxiv.1707.04674 · Cited by 21

Robust Deep Reinforcement Learning with Adversarial Attacks

Pattanaik, A., Tang, Z.-Y., Liu, S.-J., Bommannan, G., & Chowdhary, G. V. (2017) · Adaptive Agents and Multi-Agent Systems · doi:10.65109/rnct3583 · Cited by 380 · see citing works

Robust Deep Reinforcement Learning with Adversarial AttacksPreprint

Pattanaik, A., Tang, Z.-Y., Liu, S.-J., Bommannan, G., & Chowdhary, G. V. (2017) · Adaptive Agents and Multi-Agent Systems · doi:10.48550/arxiv.1712.03632 · Cited by 382 · see citing works

Adversarial Robust Deep Reinforcement Learning Requires Redefining RobustnessPreprint

Korkmaz, E. (2023) · AAAI Conference on Artificial Intelligence · doi:10.48550/arxiv.2301.07487 · Cited by 44 · see citing works

Online Robustness Training for Deep Reinforcement LearningPreprint

Fischer, M. L., Mirman, M., Stalder, S., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00887 · Cited by 12 · see citing works

Assessing the Robustness in Predictive Process Monitoring through Adversarial Attacks

Stevens, A., Smedt, J. D., Peeperkorn, J., & Weerdt, J. D. (2022) · International Conference on Process Mining (ICPM) · doi:10.1109/icpm57379.2022.9980753 · Cited by 9 · see citing works