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We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another.
Q-learning
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Horde : A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction categories and subject descriptors
Richard Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick Pilarski, Adam White, and Doina Precup · 2011
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Continuous control with deep reinforcement learning
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Contrastive explanations for reinforcement learning in terms of expected consequences
J Waa, J van Diggelen, K Bosch, and M Neerincx · 2018
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Explainable reinforcement learning via reward decomposition
Zoe Juozapaitis, Anurag Koul, Alan Fern, Martin Erwig, and Finale Doshi-Velez · 2019
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Counterfactual states for atari agents via generative deep learning
Matthew L Olson, Lawrence Neal, Fuxin Li, and Weng-Keen Wong · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, and et al · 2019
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Exploratory not explanatory: Counterfactual analysis of saliency maps for deep {rl}
Akanksha Atrey, Kaleigh Clary, and David Jensen · 2020
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Matthew Schlegel, Adam White, Andrew Patterson, and Martha White · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Explain your move: Understanding agent actions using focused feature saliency
Piyush Gupta, Nikaash Puri, Sukriti Verma, Dhruv Kayastha, Shripad Deshmukh, Balaji Krishnamurthy, and Sameer Singh · 2020
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