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Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems.
Benchmarking batch deep reinforcement learning algorithms
Scott Fujimoto, Edoardo Conti, Mohammad Ghavamzadeh, and Joelle Pineau · 1910
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SS Vallender · 1974
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Least squares quantization in pcm
Stuart Lloyd · 1982
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Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra · 1998
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
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X-means: Extending k-means with efficient estimation of the number of clusters
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
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The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
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Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander J. Smola · 2017
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Visualizing and understanding atari agents
Samuel Greydanus, Anurag Koul, Jonathan Dodge, and Alan Fern · 2018
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Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al · 2018
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Transparency and explanation in deep reinforcement learning neural networks
Rahul Iyer, Yuezhang Li, Huao Li, Michael Lewis, Ramitha Sundar, and Katia Sycara · 2018
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Anurag Koul, Sam Greydanus, and Alan Fern · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Deep structural causal models for tractable counterfactual inference
Nick Pawlowski, Daniel Coelho de Castro, and Ben Glocker · 2020
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Explainable reinforcement learning: A survey
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Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
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Transfer learning in deep reinforcement learning: A survey
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Petros Christodoulou · 2019
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Distilling deep reinforcement learning policies in soft decision trees
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Counterfactual visual explanations
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Stabilizing off-policy q-learning via bootstrapping error reduction
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Andrei Novikov · 2019
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Re-understanding finite-state representations of recurrent policy networks
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Offline reinforcement learning as one big sequence modeling problem
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Offline reinforcement learning with fisher divergence critic regularization
Ilya Kostrikov, Rob Fergus, Jonathan Tompson, and Ofir Nachum · 2021
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The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2062
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