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The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL).
La naissance de [’intelligence chez venfant. delachaux.[jcg]
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Alison Gopnik, Andrew N Meltzoff, and Patricia K Kuhl · 1999
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y Ng, Daishi Harada, and Stuart Russell · 1999
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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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Intrinsic and extrinsic motivations: Classic definitions and new directions
Richard M Ryan and Edward L Deci · 2000
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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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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Autonomous mental development by robots and animals
Juyang Weng, James McClelland, Alex Pentland, Olaf Sporns, Ida Stockman, Mriganka Sur, and Esther Thelen · 2001
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R-max-a general polynomial time algorithm for near-optimal reinforcement learning
Ronen I Brafman and Moshe Tennenholtz · 2002
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Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
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A self-organising network that grows when required
Stephen Marsland, Jonathan Shapiro, and Ulrich Nehmzow · 2002
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Q-cut—dynamic discovery of sub-goals in reinforcement learning
Ishai Menache, Shie Mannor, and Nahum Shimkin · 2002
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Slow feature analysis: Unsupervised learning of invariances
Laurenz Wiskott and Terrence J Sejnowski · 2002
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Intrinsically motivated learning of hierarchical collections of skills
Andrew G Barto, Satinder Singh, and Nuttapong Chentanez · 2004
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Intrinsically motivated reinforcement learning
Nuttapong Chentanez, Andrew G Barto, and Satinder P Singh · 2005
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Empowerment: A universal agent-centric measure of control
Alexander S Klyubin, Daniel Polani, and Chrystopher L Nehaniv · 2005
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Ongoing emergence: A core concept in epigenetic robotics
Christopher Prince, Nathan Helder, and George Hollich · 2005
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Bayesian surprise attracts human attention
Laurent Itti and Pierre F Baldi · 2006
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Maximization of potential information flow as a universal utility for collective behaviour
Philippe Capdepuy, Daniel Polani, and Chrystopher L Nehaniv · 2007
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The minimum description length principle
Peter D Grünwald · 2007
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Intrinsic motivation systems for autonomous mental development
Pierre-Yves Oudeyer, Frdric Kaplan, and Verena V Hafner · 2007
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Simple algorithmic principles of discovery, subjective beauty, selective attention, curiosity & creativity
Jürgen Schmidhuber · 2007
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Multi-task reinforcement learning: a hierarchical bayesian approach
Aaron Wilson, Alan Fern, Soumya Ray, and Prasad Tadepalli · 2007
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Exploiting open-endedness to solve problems through the search for novelty
Joel Lehman and Kenneth O Stanley · 2008
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How can we define intrinsic motivation?
Pierre-Yves Oudeyer and Frederic Kaplan · 2008
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Driven by compression progress: A simple principle explains essential aspects of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes
Jürgen Schmidhuber · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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The free-energy principle: a rough guide to the brain?
Karl Friston · 2009
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Multi-task reinforcement learning in partially observable stochastic environments
Hui Li, Xuejun Liao, and Lawrence Carin · 2009
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What is intrinsic motivation? a typology of computational approaches
Pierre-Yves Oudeyer and Frederic Kaplan · 2009
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The free-energy principle: a unified brain theory?
Karl Friston · 2010
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Exploring parameter space in reinforcement learning
Thomas Rückstiess, Frank Sehnke, Tom Schaul, Daan Wierstra, Yi Sun, and Jürgen Schmidhuber · 2010
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Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Jürgen Schmidhuber · 2010
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Intrinsically motivated reinforcement learning: An evolutionary perspective
Satinder Singh, Richard L Lewis, Andrew G Barto, and Jonathan Sorg · 2010
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Early-stage vision of composite scenes for spatial learning and navigation
Olivier L Georgeon, James B Marshall, and Pierre-Yves R Ronot · 2011
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Practical variational inference for neural networks
Alex Graves · 2011
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Learning like a baby: a survey of artificial intelligence approaches
Frank Guerin · 2011
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Elements of information theory
Thomas M Cover and Joy A Thomas · 2012
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Curriculum learning for motor skills
Andrej Karpathy and Michiel Van De Panne · 2012
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Decentralized pomdps
Frans A Oliehoek · 2012
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An information-theoretic approach to curiosity-driven reinforcement learning
Susanne Still and Doina Precup · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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A unified model of the joint development of disparity selectivity and vergence control
Yu Zhao, Constantin A Rothkopf, Jochen Triesch, and Bertram E Shi · 2012
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Intrinsically motivated learning systems: an overview
Gianluca Baldassarre and Marco Mirolli · 2013
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Active learning of inverse models with intrinsically motivated goal exploration in robots
Adrien Baranes and Pierre-Yves Oudeyer · 2013
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Intrinsic motivation and reinforcement learning
Andrew G Barto · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Whatever next? predictive brains, situated agents, and the future of cognitive science
Andy Clark · 2013
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Learning and exploration in action-perception loops
Daniel Ying-Jeh Little and Friedrich Tobias Sommer · 2013
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Powerplay: Training an increasingly general problem solver by continually searching for the simplest still unsolvable problem
Jürgen Schmidhuber · 2013
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Theories and computational models of affordance and mirror systems: an integrative review
Serge Thill, Daniele Caligiore, Anna M Borghi, Tom Ziemke, and Gianluca Baldassarre · 2013
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Imitation learning based on an intrinsic motivation mechanism for efficient coding
Jochen Triesch · 2013
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Skip context tree switching
Marc Bellemare, Joel Veness, and Erik Talvitie · 2014
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Active contextual policy search
Alexander Fabisch and Jan Hendrik Metzen · 2014
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Curiosity driven reinforcement learning for motion planning on humanoids
Mikhail Frank, Jürgen Leitner, Marijn Stollenga, Alexander Förster, and Jürgen Schmidhuber · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Changing the environment based on empowerment as intrinsic motivation
Christoph Salge, Cornelius Glackin, and Daniel Polani · 2014
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Empowerment–an introduction
Christoph Salge, Cornelius Glackin, and Daniel Polani · 2014
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Autonomous learning of smooth pursuit and vergence through active efficient coding
TN Vikram, Céline Teulière, Chong Zhang, Bertram E Shi, and Jochen Triesch · 2014
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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 · 2015
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Autonomous learning of state representations for control: An emerging field aims to autonomously learn state representations for reinforcement learning agents from their real-world sensor observations
Wendelin Böhmer, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller, and Klaus Obermayer · 2015
Cited alongside, same era.
Modeling biological agents beyond the reinforcement-learning paradigm
Olivier L Georgeon, Rémi C Casado, and Laetitia A Matignon · 2015
Cited alongside, same era.
Bayesian reinforcement learning: A survey
Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar, et al · 2015
Cited alongside, same era.
Learning state representations with robotic priors
Rico Jonschkowski and Oliver Brock · 2015
Cited alongside, same era.
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
Cited alongside, same era.
A unified strategy for implementing curiosity and empowerment driven reinforcement learning
Ildefons Magrans de Abril and Ryota Kanai · 2018
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Integrating state representation learning into deep reinforcement learning
Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška · 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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Automatic goal generation for reinforcement learning agents
Carlos Florensa, David Held, Xinyang Geng, and Pieter Abbeel · 2018
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An introduction to deep reinforcement learning
Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G Bellemare, Joelle Pineau, et al · 2018
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Multiobjective reinforcement learning: A comprehensive overview
Chunming Liu, Xin Xu, and Dewen Hu · 2015
Cited alongside, same era.
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, et al · 2015
Cited alongside, same era.
Variational information maximisation for intrinsically motivated reinforcement learning
Shakir Mohamed and Danilo Jimenez Rezende · 2015
Cited alongside, same era.
Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
Cited alongside, same era.
Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
Cited alongside, same era.
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
Cited alongside, same era.
Incentivizing exploration in reinforcement learning with deep predictive models
Bradly C Stadie, Sergey Levine, and Pieter Abbeel · 2015
Cited alongside, same era.
Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2018
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Learning an embedding space for transferable robot skills
Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, and Martin A. Riedmiller · 2018
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Deep q-learning from demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, et al · 2018
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Inequity aversion resolves intertemporal social dilemmas
Edward Hughes, Joel Z Leibo, Matthew G Philips, Karl Tuyls, Edgar A Duéñez-Guzmán, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin R McKee, Raphael Koster, et al · 2018
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Curiosity driven exploration of learned disentangled goal spaces
Adrien Laversanne-Finot, Alexandre Péré, and Pierre-Yves Oudeyer · 2018
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State representation learning for control: An overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean-Franois Goudou, and David Filliat · 2018
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Count-based exploration with the successor representation
Marlos C Machado, Marc G Bellemare, and Michael Bowling · 2018
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Unicorn: Continual learning with a universal, off-policy agent
Daniel J Mankowitz, Augustin Žídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, and Tom Schaul · 2018
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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang (Shane) Gu, Honglak Lee, and Sergey Levine · 2018
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Visual reinforcement learning with imagined goals
Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Unsupervised learning of goal spaces for intrinsically motivated goal exploration
Alexandre Péré, Sébastien Forestier, Olivier Sigaud, and Pierre-Yves Oudeyer · 2018
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Learning by playing solving sparse reward tasks from scratch
Martin A. Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Vlad Mnih, Nicolas Heess, and Jost Tobias Springenberg · 2018
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Learning abstract options
Matthew Riemer, Miao Liu, and Gerald Tesauro · 2018
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Episodic curiosity through reachability
Nikolay Savinov, Anton Raichuk, Raphaël Marinier, Damien Vincent, Marc Pollefeys, Timothy Lillicrap, and Sylvain Gelly · 2018
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Disentangling controllable and uncontrollable factors of variation by interacting with the world
Yoshihide Sawada · 2018
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Diversity-driven extensible hierarchical reinforcement learning
Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, and Mai Xu · 2018
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Deep curiosity search: Intra-life exploration can improve performance on challenging deep reinforcement learning problems
Christopher Stanton and Jeff Clune · 2018
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Learning goal embeddings via self-play for hierarchical reinforcement learning
Sainbayar Sukhbaatar, Emily Denton, Arthur Szlam, and Rob Fergus · 2018
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Successor options: An option discovery algorithm for reinforcement learning
Manan Tomar, Rahul Ramesh, and Balaraman Ravindran · 2018
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Master-slave curriculum design for reinforcement learning
Yuechen Wu, Wei Zhang, and Ke Song · 2018
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Decoupling dynamics and reward for transfer learning
Amy Zhang, Harsh Satija, and Joelle Pineau · 2018
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The journey is the reward: Unsupervised learning of influential trajectories
Jonathan Binas, Sherjil Ozair, and Yoshua Bengio · 2019
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Large-scale study of curiosity-driven learning
Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, and Alexei A. Efros · 2019
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Symmetry-based disentangled representation learning requires interaction with environments
Hugo Caselles-Dupré, Michael Garcia-Ortiz, and David Filliat · 2019
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Learning navigation behaviors end-to-end with autorl
Hao-Tien Lewis Chiang, Aleksandra Faust, Marek Fiser, and Anthony Francis · 2019
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Hypothesis-driven skill discovery for hierarchical deep reinforcement learning
Caleb Chuck, Supawit Chockchowwat, and Scott Niekum · 2019
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Curious: Intrinsically motivated modular multi-goal reinforcement learning
Cédric Colas, Pierre-Yves Oudeyer, Olivier Sigaud, Pierre Fournier, and Mohamed Chetouani · 2019
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Feature control as intrinsic motivation for hierarchical reinforcement learning
Nat Dilokthanakul, Christos Kaplanis, Nick Pawlowski, and Murray Shanahan · 2019
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Go-explore: a new approach for hard-exploration problems
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2019
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Self-supervised learning of image embedding for continuous control
Carlos Florensa, Jonas Degrave, Nicolas Heess, Jost Tobias Springenberg, and Martin Riedmiller · 2019
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Clic: Curriculum learning and imitation for feature control in non-rewarding environments
Pierre Fournier, Olivier Sigaud, Mohamed Chetouani, and Cédric Colas · 2019
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Learning actionable representations with goal conditioned policies
Dibya Ghosh, Abhishek Gupta, and Sergey Levine · 2019
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Learning gentle object manipulation with curiosity-driven deep reinforcement learning
Sandy H Huang, Martina Zambelli, Jackie Kay, Murilo F Martins, Yuval Tassa, Patrick M Pilarski, and Raia Hadsell · 2019
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Coordinated exploration via intrinsic rewards for multi-agent reinforcement learning
Shariq Iqbal and Fei Sha · 2019
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Social influence as intrinsic motivation for multi-agent deep reinforcement learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro Ortega, Dj Strouse, Joel Z Leibo, and Nando De Freitas · 2019
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EMI: Exploration with mutual information
Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, and Hyun Oh Song · 2019
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Curiosity-bottleneck: Exploration by distilling task-specific novelty
Youngjin Kim, Wontae Nam, Hyunwoo Kim, Ji-Hoon Kim, and Gunhee Kim · 2019
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Variational state encoding as intrinsic motivation in reinforcement learning
Martin Klissarov, Riashat Islam, Khimya Khetarpal, and Doina Precup · 2019
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Efficient exploration via state marginal matching
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, and Ruslan Salakhutdinov · 2019
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Hierarchical reinforcement learning with hindsight
Andrew Levy, Robert Platt, and Kate Saenko · 2019
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Adapting behaviour via intrinsic reward: A survey and empirical study
Cam Linke, Nadia M Ady, Martha White, Thomas Degris, and Adam White · 2019
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Near-optimal representation learning for hierarchical reinforcement learning
Ofir Nachum, Shixiang Gu, Honglak Lee, and Sergey Levine · 2019
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Why does hierarchy (sometimes) work so well in reinforcement learning?
Ofir Nachum, Haoran Tang, Xingyu Lu, Shixiang Gu, Honglak Lee, and Sergey Levine · 2019
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Predictive learning: its key role in early cognitive development
Yukie Nagai · 2019
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Rethinking continual learning for autonomous agents and robots
German I Parisi and Christopher Kanan · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Self-supervised exploration via disagreement
Deepak Pathak, Dhiraj Gandhi, and Abhinav Gupta · 2019
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Skew-fit: State-covering self-supervised reinforcement learning
Vitchyr H Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine · 2019
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Unsupervised methods for subgoal discovery during intrinsic motivation in model-free hierarchical reinforcement learning
Jacob Rafati and David C Noelle · 2019
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Antonin Raffin, Ashley Hill, Kalifou René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, and David Filliat · 2019
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Intrinsically motivated exploration for automated discovery of patterns in morphogenetic systems
Chris Reinke, Mayalen Etcheverry, and Pierre-Yves Oudeyer · 2019
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Autonomous reinforcement learning of multiple interrelated tasks
Vieri Giuliano Santucci, Gianluca Baldassarre, and Emilio Cartoni · 2019
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Computational mechanisms of curiosity and goal-directed exploration
Philipp Schwartenbeck, Johannes Passecker, Tobias U Hauser, Thomas HB FitzGerald, Martin Kronbichler, and Karl J Friston · 2019
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Artificial development by reinforcement learning can benefit from multiple motivations
Friedhelm Schwenker and Guenther Palm · 2019
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Dynamics-aware unsupervised discovery of skills
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2019
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Model-based active exploration
Pranav Shyam, Wojciech Jaskowski, and Faustino Gomez · 2019
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Mega-reward: Achieving human-level play without extrinsic rewards
Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Shangtong Zhang, and Mai Xu · 2019
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Benchmarking bonus-based exploration methods on the arcade learning environment
Adrien Ali Taïga, William Fedus, Marlos C Machado, Aaron Courville, and Marc G Bellemare · 2019
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Self-supervised learning of distance functions for goal-conditioned reinforcement learning
Srinivas Venkattaramanujam, Eric Crawford, Thang Doan, and Doina Precup · 2019
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Learning latent state representation for speeding up exploration
Giulia Vezzani, Abhishek Gupta, Lorenzo Natale, and Pieter Abbeel · 2019
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Unsupervised control through non-parametric discriminative rewards
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, and Volodymyr Mnih · 2019
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Scheduled intrinsic drive: A hierarchical take on intrinsically motivated exploration
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Curiosity-driven experience prioritization via density estimation
Rui Zhao and Volker Tresp · 2019
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Vision-based robot navigation through combining unsupervised learning and hierarchical reinforcement learning
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