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We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability.
A cognitive model of planning
Barbara Hayes-Roth and Frederick Hayes-Roth · 1979
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A possibility for implementing curiosity and boredom in model-building neural controllers
Jürgen Schmidhuber · 1990
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Elements of information theory
Thomas M Cover and Joy A Thomas · 1991
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 1999
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Automatic discovery of subgoals in reinforcement learning using diverse density
Amy McGovern and Andrew G. Barto · 2001
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Geometric clustering using the information bottleneck method
Susanne Still, William Bialek, and Léon Bottou · 2004
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Information bottleneck for gaussian variables
Gal Chechik, Amir Globerson, Naftali Tishby, and Yair Weiss · 2005
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Relevant information in optimized persistence vs. progeny strategies
Daniel Polani, Chrystopher L Nehaniv, Thomas Martinetz, and Jan T Kim · 2006
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
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Grounding subgoals in information transitions
Sander G van Dijk and Daniel Polani · 2011
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Exploration in model-based reinforcement learning by empirically estimating learning progress
Manuel Lopes, Tobias Lang, Marc Toussaint, and Pierre-Yves Oudeyer · 2012
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Empowerment – an introduction
Christoph Salge, Cornelius Glackin, and Daniel Polani · 2013
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
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Relevant sparse codes with variational information bottleneck
Matthew Chalk, Olivier Marre, and Gasper Tkacik · 2016
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The information bottleneck and geometric clustering
D J Strouse and David J Schwab · 2017
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Distral: Robust multitask reinforcement learning
Yee Teh, Victor Bapst, Wojciech M Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
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Variational option discovery algorithms
Joshua Achiam, Harrison Edwards, Dario Amodei, and Pieter Abbeel · 2018
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Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2016
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beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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A laplacian framework for option discovery in reinforcement learning
Marios Machado, Marc Bellemare, and Michael Bowling · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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Pytorch implementations of reinforcement learning algorithms
Ilya Kostrikov · 2018
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Learning to share and hide intentions using information regularization
D J Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, and David Schwab · 2018
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Efficient compression in color naming and its evolution
Noga Zaslavsky, Charles Kemp, Terry Regier, and Naftali Tishby · 2018
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InfoBot: Transfer and exploration via the information bottleneck
Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, Hugo Larochelle, Yoshua Bengio, and Sergey Levine · 2019
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