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Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples.
Overlapping neural systems represent cognitive effort and reward anticipation
Eliana Vassena, Massimo Silvetti, Carsten N. Boehler, Eric Achten, Wim Fias, and Tom Verguts · 1901
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Introduciton to the theory of neural computation
John Hertz, Anders Krogh, and Richard Palmer · 1991
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Actor-Critic Algorithms
Vijay Konda and John N Tsitsiklis · 2000
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Prefrontal-cingulate interactions in action monitoring
William J Gehring and Robert T Knight · 2000
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An Integrative Theory of Prefrontal Cortex Funciton
Earl K Miller and Jonathan D Cohen · 2001
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Primate anterior cingluate cortex: where motor control, drive and cognition interface
Tomas Paus · 2001
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Learned Predictions of Error Likelihood in the Anterior Cingulate Cortex
Joshua W. Brown and Todd S. Braver · 2005
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Role of the lateral prefrontal cortex in executive behavioral control
Jun Tanji and Eiji Hoshi · 2008
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Visualizing higher-layer features of a deep network
Dumitru Erhan, Y Bengio, Aaron Courville, and Pascal Vincent · 2009
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Inhibition recruitment in prefrontal cortex during sleep spindles and gating of hippocampal inputs
a. Peyrache, F. P. Battaglia, and a. Destexhe · 2011
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A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
Ivo Grondman, Lucian Busoniu, Gabriel A D Lopes, and Robert Babuska · 2012
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Neural dynamics and circuit mechanisms of decision-making
Xiao Jing Wang · 2012
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Visualizing and Understanding Convolutional Networks arXiv:1311.2901v3
Matthew Zeiler and Rob Fergus · 2014
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Complementary learning systems
Randall C. O’Reilly, Rajan Bhattacharyya, Michael D. Howard, and Nicholas Ketz · 2014
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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, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Understanding Neural Networks Through Deep Visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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Hidden factors and hidden topics: understanding rating dimensions with review text
J McAuley and J Leskovec · 2015
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The Role of Prefrontal Cortex in Working Memory: A Mini Review
Antonio H. Lara and Jonathan D. Wallis · 2015
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Prefrontal-Hippocampal Interactions in Memory and Emotion
Jingji Jin and Stephen Maren · 2015
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Contrastive explanations for reinforcement learning in terms of expected consequences
J Van Der Waa, J Van Diggelen, and M Neerincx · 2017
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Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth C. Fong and Andrea Vedaldi · 2017
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Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
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EXPLAINABLE ARTIFICIAL INTELLIGENCE: UNDERSTANDING, VISUALIZING AND INTERPRETING DEEP LEARNING MODELS Wojciech
Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller · 2017
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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Edward Chanan, Gregory Yang, Zachary DeVito, Alban Lin, Zeming Desmaison, Luca Antiga, and Adam Lerer · 2017
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Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
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David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
Cited alongside, same era.
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih, Mehdi Mirza, Alex Graves, Tim Harley, Timothy P Lillicrap, David Silver, and Koray Kavukcuoglu · 2016
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Control of Memory, Active Perception, and Action in Minecraft
Junhyuk Oh, Valliappa Chockalingam, Satinder Singh, and Honglak Lee · 2016
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Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, and Nando de Freitas · 2016
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The Mythos of Model Interpretability
Zachary C. Lipton · 2016
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OpenAI Gym
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Reinforcement Learning: An Introduction
Richard S Sutton and Andrew G Barto · 2017
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Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, Vikash Kumar, and Wojciech Zaremba · 2018
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Please Stop Explaining Black Box Models for High Stakes Decisions
Cynthia Rudin · 2018
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Stakeholders in Explainable AI
Alun Preece, Dan Harborne, Dave Braines, Richard Tomsett, and Supriyo Chakraborty · 2018
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The building blocks of interpretability
Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, and Alexander Mordvintsev · 2018
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Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus Robert Müller · 2018
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Pytorh reinforcement examples, 2018
Pytorch-team · 2018
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Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller · 2019
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Explaining Explanations in AI
Brent Mittelstadt, Chris Russell, and Sandra Wachter · 2019
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