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1903.09708
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Explaining Reinforcement Learning to Mere Mortals: An Empirical Study
22 March 2019
Andrew Anderson
Jonathan Dodge
Amrita Sadarangani
Zoe Juozapaitis
E. Newman
Jed Irvine
Souti Chattopadhyay
Alan Fern
Margaret Burnett
FAtt
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Papers citing
"Explaining Reinforcement Learning to Mere Mortals: An Empirical Study"
27 / 27 papers shown
Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies
Armin Kekić
Jan Schneider
Dieter Büchler
Bernhard Scholkopf
M. Besserve
CML
169
1
0
20 Jul 2025
SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks
AAAI Conference on Artificial Intelligence (AAAI), 2024
Yongyan Wen
Siyuan Li
Rongchang Zuo
Lei Yuan
Hangyu Mao
P. Liu
482
1
0
19 Nov 2024
How to Measure Human-AI Prediction Accuracy in Explainable AI Systems
Sujay Koujalgi
Andrew Anderson
Iyadunni Adenuga
Shikha Soneji
Rupika Dikkala
...
Leo Soccio
Sourav Panda
Rupak Kumar Das
Margaret Burnett
Jonathan Dodge
235
3
0
23 Aug 2024
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving
Shahin Atakishiyev
Mohammad Salameh
Randy Goebel
722
15
0
18 Mar 2024
Experiments with Encoding Structured Data for Neural Networks
Sujay Nagesh Koujalgi
Jonathan Dodge
271
0
0
15 Feb 2024
Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes
Yotam Amitai
Yael Septon
Ofra Amir
CML
270
17
0
18 Dec 2023
An Information Bottleneck Characterization of the Understanding-Workload Tradeoff
Conference on Fairness, Accountability and Transparency (FAccT), 2023
Lindsay M. Sanneman
Mycal Tucker
Julie A. Shah
276
8
0
11 Oct 2023
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples
Neural Information Processing Systems (NeurIPS), 2023
Hao Sun
Alihan Huyuk
Daniel Jarrett
M. Schaar
OffRL
438
10
0
11 Oct 2023
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
Practical Applications of Agents and Multi-Agent Systems (PAAMS), 2022
Yael Septon
Tobias Huber
Elisabeth André
Ofra Amir
FAtt
177
15
0
21 Oct 2022
Experiential Explanations for Reinforcement Learning
Amal Alabdulkarim
Madhuri Singh
Gennie Mansi
Kaely Hall
Mark O. Riedl
Mark O. Riedl
OffRL
579
7
0
10 Oct 2022
Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications
ACM Computing Surveys (ACM CSUR), 2022
Tom Hickling
Abdelhafid Zenati
Nabil Aouf
P. Spencer
XAI
AI4TS
430
45
0
05 Jul 2022
A Survey of Explainable Reinforcement Learning
Stephanie Milani
Nicholay Topin
Manuela Veloso
Fei Fang
XAI
LRM
318
65
0
17 Feb 2022
Explaining Reinforcement Learning Policies through Counterfactual Trajectories
Julius Frost
Olivia Watkins
Eric Weiner
Pieter Abbeel
Trevor Darrell
Bryan A. Plummer
Kate Saenko
OffRL
202
8
0
29 Jan 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
ACM Computing Surveys (ACM CSUR), 2022
Meike Nauta
Jan Trienes
Shreyasi Pathak
Elisa Nguyen
Michelle Peters
Yasmin Schmitt
Jorg Schlotterer
M. V. Keulen
C. Seifert
ELM
XAI
806
633
0
20 Jan 2022
Interpretable Learned Emergent Communication for Human-Agent Teams
IEEE Transactions on Cognitive and Developmental Systems (IEEE TCDS), 2022
Seth Karten
Mycal Tucker
Huao Li
Siva Kailas
Michael Lewis
Katia Sycara
AI4CE
313
13
0
19 Jan 2022
Explaining Reward Functions to Humans for Better Human-Robot Collaboration
Lindsay M. Sanneman
J. Shah
164
5
0
08 Oct 2021
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods
AAAI Conference on Artificial Intelligence (AAAI), 2021
Nicholay Topin
Stephanie Milani
Fei Fang
Manuela Veloso
OffRL
254
45
0
25 Feb 2021
Benchmarking Perturbation-based Saliency Maps for Explaining Atari Agents
Frontiers in Artificial Intelligence (Front. Artif. Intell.), 2021
Tobias Huber
Benedikt Limmer
Elisabeth André
FAtt
303
23
0
18 Jan 2021
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning
Frontiers in Artificial Intelligence (FAI), 2020
Silvan Mertes
Tobias Huber
Katharina Weitz
Alexander Heimerl
Elisabeth André
GAN
AAML
MedIm
406
110
0
22 Dec 2020
Measure Utility, Gain Trust: Practical Advice for XAI Researcher
B. Pierson
M. Glenski
William I. N. Sealy
Dustin L. Arendt
168
29
0
27 Sep 2020
Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario
Francisco Cruz
Richard Dazeley
Peter Vamplew
Ithan Moreira
433
45
0
24 Jun 2020
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
Tobias Huber
Katharina Weitz
Elisabeth André
Ofra Amir
FAtt
411
71
0
18 May 2020
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition
Mahsan Nourani
Chiradeep Roy
Tahrima Rahman
Eric D. Ragan
Nicholas Ruozzi
Vibhav Gogate
AAML
246
21
0
05 May 2020
Tradeoff-Focused Contrastive Explanation for MDP Planning
IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2020
Roykrong Sukkerd
R. Simmons
David Garlan
232
28
0
27 Apr 2020
The Emerging Landscape of Explainable AI Planning and Decision Making
International Joint Conference on Artificial Intelligence (IJCAI), 2020
Tathagata Chakraborti
S. Sreedharan
S. Kambhampati
294
127
0
26 Feb 2020
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
International Conference on Learning Representations (ICLR), 2020
S. Sreedharan
Utkarsh Soni
Mudit Verma
Siddharth Srivastava
S. Kambhampati
676
39
0
04 Feb 2020
Counterfactual States for Atari Agents via Generative Deep Learning
Matthew Lyle Olson
Lawrence Neal
Fuxin Li
Weng-Keen Wong
CML
175
28
0
27 Sep 2019
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