Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning

9 December 2019

Papers citing "Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning"

8 / 58 papers shown

Title
Reconstructing Actions To Explain Deep Reinforcement Learning Xuan Chen Zifan Wang Yucai Fan Bonan Jin Piotr (Peter) Mardziel Carlee Joe-Wong Anupam Datta FAtt 13 2 0 17 Sep 2020
"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games Mikhail Jacob Sam Devlin Katja Hofmann LLMAG AI4CE 30 16 0 01 Sep 2020
Evaluating the Performance of Reinforcement Learning Algorithms Scott M. Jordan Yash Chandak Daniel Cohen Mengxue Zhang Philip S. Thomas 16 46 0 30 Jun 2020
Automatic Discovery of Interpretable Planning Strategies Julian Skirzyñski Frederic Becker Falk Lieder 21 15 0 24 May 2020
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations S. Sreedharan Utkarsh Soni Mudit Verma Siddharth Srivastava S. Kambhampati 76 30 0 04 Feb 2020
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 13 132 0 20 Dec 2019
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation Tianhong Dai Kai Arulkumaran Tamara Gerbert Samyakh Tukra Feryal M. P. Behbahani Anil Anthony Bharath 17 27 0 18 Dec 2019
MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning Raghunandan Rajan Jessica Lizeth Borja Diaz Suresh Guttikonda Fabio Ferreira André Biedenkapp Jan Ole von Hartz Frank Hutter 33 3 0 17 Sep 2019