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Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal policies.
Neural reinforcement learning for behaviour synthesis
Claude F. Touzet · 1997
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Policy invariance under reward transformations: Theory and application to reward shaping
A. Ng, Daishi Harada, and Stuart J. Russell · 1999
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Attribute and simile classifiers for face verification
Neeraj Kumar, Alexander C. Berg, Peter N. Belhumeur, and Shree K. Nayar · 2009
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Learning to detect unseen object classes by between-class attribute transfer
Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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The arcade learning environment: An evaluation platform for general agents (extended abstract)
Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2012
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller · 2013
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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 A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Kumar Divvala, Ross B. Girshick, and Ali Farhadi · 2016
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Prioritized experience replay
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2016
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Deep reinforcement learning with double q-learning
Hado van Hasselt, Arthur Guez, and David Silver · 2016
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
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Value alignment or misalignment – what will keep systems accountable?
Thomas Arnold and Daniel Kasenberg · 2017
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The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Verifiable reinforcement learning via policy extraction
Osbert Bastani, Yewen Pu, and Armando Solar-Lezama · 2018
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Ai safety via debate
Geoffrey Irving, Paul Francis Christiano, and Dario Amodei · 2018
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents (extended abstract)
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, and Michael Bowling · 2018
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Programmatically interpretable reinforcement learning
Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, and Swarat Chaudhuri · 2018
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Visual rationalizations in deep reinforcement learning for atari games
Laurens Weitkamp, Elise van der Pol, and Zeynep Akata · 2018
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A gradient-based split criterion for highly accurate and transparent model trees
Klaus Broelemann and Gjergji Kasneci · 2019
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Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Christopher Hesse, Taehoon Kim, and John Schulman · 2019
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Unsupervised curricula for visual meta-reinforcement learning
A. Jabri, Kyle Hsu, Benjamin Eysenbach, Abhishek Gupta, Alexei A. Efros, Sergey Levine, and Chelsea Finn · 2019
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Neural logic reinforcement learning
Zhengyao Jiang and Shan Luo · 2019
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Model-based reinforcement learning for atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, K. Czechowski, D. Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, G. Tucker, and Henryk Michalewski · 2019
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Unmasking clever hans predictors and assessing what machines really learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2019
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Vision-based robot navigation through combining unsupervised learning and hierarchical reinforcement learning
Xiaomao Zhou, Tao Bai, Yanbin Gao, and Yuntao Han · 2019
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Playing atari with six neurons (extended abstract)
Giuseppe Cuccu, Julian Togelius, and Philippe Cudré-Mauroux · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann · 2020
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Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
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Learning interpretable concept-based models with human feedback
Isaac Lage and Finale Doshi-Velez · 2020
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SPACE: unsupervised object-oriented scene representation via spatial attention and decomposition
Zhixuan Lin, Yi-Fu Wu, Skand Vishwanath Peri, Weihao Sun, Gautam Singh, Fei Deng, Jindong Jiang, and Sungjin Ahn · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, and Kristian Kersting · 2020
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Concept bottleneck model with additional unsupervised concepts
Yoshihide Sawada and Keigo Nakamura · 2022
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Interactive disentanglement: Learning concepts by interacting with their prototype representations
Wolfgang Stammer, Marius Memmel, Patrick Schramowski, and Kristian Kersting · 2022
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Explainable deep reinforcement learning: State of the art and challenges
George A. Vouros · 2022
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Neural networks are decision trees
Çağlar Aytekin · 2022
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Concept-level debugging of part-prototype networks
Andrea Bontempelli, Stefano Teso, Katya Tentori, Fausto Giunchiglia, and Andrea Passerini · 2023
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One explanation does not fit xil
Felix Friedrich, David Steinmann, and Kristian Kersting · 2023
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Curl: Contrastive unsupervised representations for reinforcement learning
A. Srinivas, Michael Laskin, and P. Abbeel · 2020
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Rationalization through concepts
Diego Antognini and Boi Faltings · 2021
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Visual explanation using attention mechanism in actor-critic-based deep reinforcement learning
Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi, and Komei Sugiura · 2021
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Symbols as a lingua franca for bridging human-ai chasm for explainable and advisable ai systems
Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma, Yantian Zha, and L. Guan · 2021
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Neuro-symbolic reinforcement learning with first-order logic
Daiki Kimura, Masaki Ono, Subhajit Chaudhury, Ryosuke Kohita, Akifumi Wachi, Don Joven Agravante, Michiaki Tatsubori, Asim Munawar, and Alexander Gray · 2021
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Objective robustness in deep reinforcement learning, 2021
Jack Koch, Lauro Langosco, Jacob Pfau, James Le, and Lee Sharkey · 2021
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Counterfactual credit assignment in model-free reinforcement learning
Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S. Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, and Rémi Munos · 2021
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Relative behavioral attributes: Filling the gap between symbolic goal specification and reward learning from human preferences
Lin Guan, Karthik Valmeekam, and Subbarao Kambhampati · 2023
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Reward design with language models
Minae Kwon, Sang Michael Xie, Kalesha Bullard, and Dorsa Sadigh · 2023
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Neuro-symbolic reasoning shortcuts: Mitigation strategies and their limitations
Emanuele Marconato, Stefano Teso, and Andrea Passerini · 2023
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Explainable reinforcement learning: A survey and comparative review
Stephanie Milani, Nicholay Topin, Manuela Veloso, and Fei Fang · 2023
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Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
Waddah Saeed and Christian W. Omlin · 2023
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Learning to intervene on concept bottlenecks
David Steinmann, Wolfgang Stammer, Felix Friedrich, and Kristian Kersting · 2023
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Leveraging explanations in interactive machine learning: An overview
Stefano Teso, Öznur Alkan, Wolfgang Stammer, and Elizabeth Daly · 2023
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Concept learning for interpretable multi-agent reinforcement learning
Renos Zabounidis, Joseph Campbell, Simon Stepputtis, Dana Hughes, and Katia P. Sycara · 2023
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Gradient boosting reinforcement learning
Benjamin Fuhrer, Chen Tessler, and Gal Dalal · 2024
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Interpretable and editable programmatic tree policies for reinforcement learning
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