Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences

Built on

Improving action selection in mdp’s via knowledge transfer
Alexander A Sherstov and Peter Stone · 2005
Earlier work this paper cites.
Reinforcement learning: A tutorial survey and recent advances
Abhijit Gosavi · 2009
Earlier work this paper cites.
Principles of explanatory debugging to personalize interactive machine learning
Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf · 2015
Earlier work this paper cites.
The mythos of model interpretability
Original
Zachary C Lipton · 2016
Earlier work this paper cites.
An unexpected unity among methods for interpreting model predictions
Original
Scott Lundberg and Su-In Lee · 2016
Earlier work this paper cites.

Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Interpreting the predictions of complex ml models by layer-wise relevance propagation
Original
Wojciech Samek, Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, and Klaus-Robert Müller · 2016
Cited alongside, same era.
Improving robot controller transparency through autonomous policy explanation
Bradley Hayes and Julie A Shah · 2017
Cited alongside, same era.
Interpretable policies for reinforcement learning by genetic programming
Original
Daniel Hein, Steffen Udluft, and Thomas A Runkler · 2017
Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
Original
Tim Miller · 2017
Cited alongside, same era.