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Current work in explainable reinforcement learning generally produces policies in the form of a decision tree over the state space.
MoËT: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees
Vasic, M.; Petrovic, A.; Wang, K.; Nikolic, M.; Singh, R.; and Khurshid, S. 2019 · 1906
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Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy
Roth, A. M.; Topin, N.; Jamshidi, P.; and Veloso, M. 2019 · 1907
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Neuronlike adaptive elements that can solve difficult learning control problems
Barto, A. G.; Sutton, R. S.; and Anderson, C. W. 1983 · 1983
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Learn to interpret Atari agents
Yang, Z.; Bai, S.; Zhang, L.; and Torr, P. H. 2018 · 1983
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Induction of decision trees
Quinlan, J. R. 1986 · 1986
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Feudal reinforcement learning
Dayan, P.; and Hinton, G. E. 1993 · 1993
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Reinforcement learning methods for continuous-time Markov decision problems
Bradtke, S.; and Duff, M. 1994 · 1994
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Exploiting structure in policy construction
Boutilier, C.; Dearden, R.; Goldszmidt, M.; et al. 1995 · 1995
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Self-improving factory simulation using continuous-time average-reward reinforcement learning
Mahadevan, S.; Marchalleck, N.; Das, T. K.; and Gosavi, A. 1997 · 1997
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Reinforcement learning with selective perception and hidden state
McCallum, R. 1997 · 1997
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Hierarchical control and learning for Markov decision processes
Parr, R. E.; and Russell, S. 1998 · 1998
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Theoretical results on reinforcement learning with temporally abstract options
Precup, D.; Sutton, R. S.; and Singh, S. 1998 · 1998
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Tree based discretization for continuous state space reinforcement learning
Uther, W. T.; and Veloso, M. M. 1998 · 1998
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Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S.; Precup, D.; and Singh, S. 1999 · 1999
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Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
Dietterich, T. G. 2000 · 2000
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Distal Explanations for Explainable Reinforcement Learning Agents
Madumal, P.; Miller, T.; Sonenberg, L.; and Vetere, F. 2020a · 2001
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Decision tree function approximation in reinforcement learning
Pyeatt, L. D.; and Howe, A. E. 2001 · 2001
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Reinforcement learning in large state spaces
Tuyls, K.; Maes, S.; and Manderick, B. 2002 · 2002
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Reinforcement Learning with Decision Trees
Pyeatt, L. D. 2003 · 2003
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Neuroevolution of Self-Interpretable Agents
Tang, Y.; Nguyen, D.; and Ha, D. 2020 · 2003
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Model compression
Buciluǎ, C.; Caruana, R.; and Niculescu-Mizil, A. 2006 · 2006
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Learning the structure of factored Markov decision processes in reinforcement learning problems
Degris, T.; Sigaud, O.; and Wuillemin, P.-H. 2006 · 2006
Cited alongside, same era.
What can I do here? A Theory of Affordances in Reinforcement Learning
Khetarpal, K.; Ahmed, Z.; Comanici, G.; Abel, D.; and Precup, D. 2020 · 2006
Cited alongside, same era.
Adaptive Building of Decision Trees by Reinforcement Learning
Preda, M. 2007 · 2007
Cited alongside, same era.
Efficient structure learning in factored-state MDPs
Strehl, A. L.; Diuk, C.; and Littman, M. L. 2007 · 2007
Cited alongside, same era.
Generalized model learning for reinforcement learning on a humanoid robot
Hester, T.; Quinlan, M.; and Stone, P. 2010 · 2010
Cited alongside, same era.
Visualizing and Understanding Atari Agents
Greydanus, S.; Koul, A.; Dodge, J.; and Fern, A. 2018 · 2018
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Stable Baselines
Hill, A.; Raffin, A.; Ernestus, M.; Gleave, A.; Kanervisto, A.; Traore, R.; Dhariwal, P.; Hesse, C.; Klimov, O.; Nichol, A.; Plappert, M.; Radford, A.; Schulman, J.; Sidor, S.; and Wu, Y. 2018 · 2018
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Transparency and Explanation in Deep Reinforcement Learning Neural Networks
Iyer, R.; Li, Y.; Li, H.; Lewis, M.; Sundar, R.; and Sycara, K. 2018 · 2018
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Learning finite state representations of recurrent policy networks
Koul, A.; Greydanus, S.; and Fern, A. 2018 · 2018
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The mythos of model interpretability
Lipton, Z. C. 2018 · 2018
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A reinforcement learning framework for explainable recommendation
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Dodson, T.; Mattei, N.; and Goldsmith, J. 2011 · 2011
Cited alongside, same era.
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Ross, S.; Gordon, G. J.; and Bagnell, J. A. 2011 · 2011
Cited alongside, same era.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T.; and Hinton, G. 2012 · 2012
Cited alongside, same era.
Playing Atari with deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; and Riedmiller, M. 2013 · 2013
Cited alongside, same era.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G. S.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Goodfellow, I.; Harp, A.; Irving, G.; Isard, M.; Jia, Y.; Jozefowicz, R.; Kaiser, L.; Kudlur, M.; Levenberg, J.; Mané, D.; Monga, R.; Moore, S.; Murray, D.; Olah, C.; Schuster, M.; Shlens, J.; Steiner, B.; Sutskever, I.; Talwar, K.; Tucker, P.; Vanhoucke, V.; Vasudevan, V.; Viégas, F.; Vinyals, O.; Warden, P.; Wattenberg, M.; Wicke, M.; Yu, Y.; and Zheng, X. 2015 · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
Cited alongside, same era.
Rusu, A. A.; Colmenarejo, S. G.; Gulcehre, C.; Desjardins, G.; Kirkpatrick, J.; Pascanu, R.; Mnih, V.; Kavukcuoglu, K.; and Hadsell, R. 2015 · 2015
Cited alongside, same era.
Wang, X.; Chen, Y.; Yang, J.; Wu, L.; Wu, Z.; and Xie, X. 2018 · 2018
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Explaining reinforcement learning to mere mortals: An empirical study
Anderson, A.; Dodge, J.; Sadarangani, A.; Juozapaitis, Z.; Newman, E.; Irvine, J.; Chattopadhyay, S.; Fern, A.; and Burnett, M. 2019 · 2019
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Towards better interpretability in deep q-networks
Annasamy, R. M.; and Sycara, K. 2019 · 2019
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Memory-Based Explainable Reinforcement Learning
Cruz, F.; Dazeley, R.; and Vamplew, P. 2019 · 2019
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Interpretable Policies for Reinforcement Learning by Genetic Programming
Hein, D.; Udluft, S.; and Runkler, T. A. 2019 · 2019
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Enhancing explainability of deep reinforcement learning through selective layer-wise relevance propagation
Huber, T.; Schiller, D.; and André, E. 2019 · 2019
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Explainable reinforcement learning via reward decomposition
Juozapaitis, Z.; Koul, A.; Fern, A.; Erwig, M.; and Doshi-Velez, F. 2019 · 2019
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Retrospective Analysis of the 2019 MineRL Competition on Sample-Efficient Reinforcement Learning Using Human Priors
Milani, S.; Topin, N.; Houghton, B.; Guss, W. H.; Mohanty, S. P.; Nakata, K.; Vinyals, O.; and Kuno, N. S. 2020 · 2019
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Interpretable Machine Learning
Molnar, C. 2019 · 2019
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Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals
Sokol, K.; and Flach, P. 2019 · 2019
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Explanation-based reward coaching to improve human performance via reinforcement learning
Tabrez, A.; Agrawal, S.; and Hayes, B. 2019 · 2019
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Generation of Policy-Level Explanations for Reinforcement Learning
Topin, N.; and Veloso, M. 2019 · 2019
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Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep RL
Atrey, A.; Clary, K.; and Jensen, D. 2020 · 2020
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Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning
Rodriguez, I. D. J.; Killian, T. W.; Son, S.; and Gombolay, M. C. 2020 · 2020
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TLdR: Policy Summarization for Factored SSP Problems Using Temporal Abstractions
Sreedharan, S.; Srivastava, S.; and Kambhampati, S. 2020 · 2020
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