Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation

18 December 2019

Feryal M. P. Behbahani

Anil Anthony Bharath

ArXiv PDF HTML

Papers citing "Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation"

50 / 76 papers shown

Title
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning Matthew Lyle Olson Roli Khanna Lawrence Neal Fuxin Li Weng-Keen Wong CML 51 71 0 29 Jan 2021
Shortcut Learning in Deep Neural Networks Robert Geirhos J. Jacobsen Claudio Michaelis R. Zemel Wieland Brendel Matthias Bethge Felix Wichmann 194 2,023 0 16 Apr 2020
How Do You Act? An Empirical Study to Understand Behavior of Deep Reinforcement Learning Agents Richard Meyes Moritz Schneider Tobias Meisen 50 2 0 07 Apr 2020
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution Nikaash Puri Sukriti Verma Piyush B. Gupta Dhruv Kayastha Shripad Deshmukh Balaji Krishnamurthy Sameer Singh FAtt AAML 31 76 0 23 Dec 2019
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations Pedro Sequeira Melinda Gervasio 31 104 0 19 Dec 2019
Dota 2 with Large Scale Deep Reinforcement Learning OpenAI OpenAI : Christopher Berner Greg Brockman Brooke Chan ... Szymon Sidor Ilya Sutskever Jie Tang Filip Wolski Susan Zhang GNN VLM CLL AI4CE LRM 108 1,811 0 13 Dec 2019
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning Akanksha Atrey Kaleigh Clary David D. Jensen FAtt LRM 52 91 0 09 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 310 42,038 0 03 Dec 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 102 6,211 0 22 Oct 2019
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning Theo Jaunet Romain Vuillemot Christian Wolf HAI 86 36 0 06 Sep 2019
Adversarial Robustness through Local Linearization Chongli Qin James Martens Sven Gowal Dilip Krishnan Krishnamurthy Dvijotham Alhussein Fawzi Soham De Robert Stanforth Pushmeet Kohli AAML 56 307 0 04 Jul 2019
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents Christian Rupprecht Cyril Ibrahim C. Pal 51 32 0 02 Apr 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks Thomas G. Dietterich OOD VLM 123 3,399 0 28 Mar 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 78 1,005 0 26 Feb 2019
Investigating Generalisation in Continuous Deep Reinforcement Learning Chenyang Zhao Olivier Sigaud F. Stulp Timothy M. Hospedales OffRL 43 48 0 19 Feb 2019
Are All Layers Created Equal? Chiyuan Zhang Samy Bengio Y. Singer 58 140 0 06 Feb 2019
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss Nicholas Frosst Nicolas Papernot Geoffrey E. Hinton 41 159 0 05 Feb 2019
Adversarial Examples Are a Natural Consequence of Test Error in Noise Nic Ford Justin Gilmer Nicholas Carlini E. D. Cubuk AAML 70 319 0 29 Jan 2019
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents F. Such Vashisht Madhavan Rosanne Liu Rui Wang Pablo Samuel Castro ... Jiale Zhi Ludwig Schubert Marc G. Bellemare Jeff Clune Joel Lehman OffRL 45 54 0 17 Dec 2018
Measuring and Characterizing Generalization in Deep Reinforcement Learning Sam Witty Jun Ki Lee Emma Tosch Akanksha Atrey Michael Littman David D. Jensen OffRL 38 60 0 07 Dec 2018
Quantifying Generalization in Reinforcement Learning K. Cobbe Oleg Klimov Christopher Hesse Taehoon Kim John Schulman OffRL 75 662 0 06 Dec 2018
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures Junhui Yin Jiayan Qiu Csaba Szepesvári Siqing Zhang Avraham Ruderman Jiyang Xie Krishnamurthy Dvijotham Zhanyu Ma N. Heess Pushmeet Kohli AAML 60 80 0 04 Dec 2018
An Introduction to Deep Reinforcement Learning Vincent François-Lavet Peter Henderson Riashat Islam Marc G. Bellemare Joelle Pineau OffRL AI4CE 123 1,242 0 30 Nov 2018
A Closer Look at Deep Policy Gradients Andrew Ilyas Logan Engstrom Shibani Santurkar Dimitris Tsipras Firdaus Janoos Larry Rudolph Aleksander Madry 51 50 0 06 Nov 2018
Assessing Generalization in Deep Reinforcement Learning Charles Packer Katelyn Gao Jernej Kos Philipp Krahenbuhl V. Koltun D. Song OffRL 95 235 0 29 Oct 2018
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience Yevgen Chebotar Ankur Handa Viktor Makoviychuk Miles Macklin J. Issac Nathan D. Ratliff Dieter Fox 70 503 0 12 Oct 2018
Learning Dexterous In-Hand Manipulation OpenAI OpenAI Marcin Andrychowicz Bowen Baker Maciek Chociej Rafal Jozefowicz ... Szymon Sidor Joshua Tobin Peter Welinder Lilian Weng Wojciech Zaremba 95 1,865 0 01 Aug 2018
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning Amy Zhang Nicolas Ballas Joelle Pineau CLL OffRL 70 177 0 20 Jun 2018
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research Matthias Plappert Marcin Andrychowicz Alex Ray Bob McGrew Bowen Baker ... Joshua Tobin Maciek Chociej Peter Welinder Vikash Kumar Wojciech Zaremba 59 562 0 26 Feb 2018
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction Leland McInnes John Healy James Melville 146 9,312 0 09 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 101 3,922 0 06 Feb 2018
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 89 683 0 02 Nov 2017
Visualizing and Understanding Atari Agents S. Greydanus Anurag Koul Jonathan Dodge Alan Fern FAtt 98 345 0 31 Oct 2017
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization Xue Bin Peng Marcin Andrychowicz Wojciech Zaremba Pieter Abbeel 93 1,355 0 18 Oct 2017
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping Konstantinos Bousmalis A. Irpan Paul Wohlhart Yunfei Bai Matthew Kelcey ... Julian Ibarz P. Pastor K. Konolige Sergey Levine Vincent Vanhoucke OOD 71 654 0 22 Sep 2017
A Brief Survey of Deep Reinforcement Learning Kai Arulkumaran M. Deisenroth Miles Brundage Anil Anthony Bharath OffRL 106 2,792 0 19 Aug 2017
Proximal Policy Optimization Algorithms John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov OffRL 259 18,685 0 20 Jul 2017
Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task Stephen James Andrew J. Davison Edward Johns 194 275 0 07 Jul 2017
Hindsight Experience Replay Marcin Andrychowicz Dwight Crow Alex Ray Jonas Schneider Rachel Fong Peter Welinder Bob McGrew Joshua Tobin Pieter Abbeel Wojciech Zaremba OffRL 233 2,307 0 05 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 241 11,962 0 19 Jun 2017
An Entropy-based Pruning Method for CNN Compression Jian-Hao Luo Jianxin Wu 32 180 0 19 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 690 21,613 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 141 3,848 0 10 Apr 2017
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World Joshua Tobin Rachel Fong Alex Ray Jonas Schneider Wojciech Zaremba Pieter Abbeel 191 2,948 0 20 Mar 2017
Robust Adversarial Reinforcement Learning Lerrel Pinto James Davidson Rahul Sukthankar Abhinav Gupta OOD 83 848 0 08 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 147 5,920 0 04 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 359 3,742 0 28 Feb 2017
Investigating the influence of noise and distractors on the interpretation of neural networks Pieter-Jan Kindermans Kristof T. Schütt K. Müller Sven Dähne FAtt 49 125 0 22 Nov 2016
CAD2RL: Real Single-Image Flight without a Single Real Image Fereshteh Sadeghi Sergey Levine SSL 295 814 0 13 Nov 2016
Sim-to-Real Robot Learning from Pixels with Progressive Nets Andrei A. Rusu Matej Vecerík Thomas Rothörl N. Heess Razvan Pascanu R. Hadsell 70 532 0 13 Oct 2016