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Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback.
Reinforcement today
Burrhus F Skinner · 1958
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Mind in Society: Development of Higher Psychological Processes
Lev Semenovich Vygotsky · 1978
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Filtered beam search in scheduling
Peng Si Ow and Thomas E Morton · 1988
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Genetic Algorithms in Search, Optimization and Machine Learning
David E Goldberg · 1989
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Learning from delayed rewards
Christopher John Cornish Hellaby Watkins · 1989
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The ant system: An autocatalytic optimizing process
Marco Dorigo, Vittorio Maniezzo, and Alberto Colorni · 1991
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Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
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Learning and development in neural networks: The importance of starting small
Jeffrey L Elman · 1993
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Neural network learning control of robot manipulators using gradually increasing task difficulty
Terence D Sanger · 1994
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Learning to solve complex planning problems: Finding useful auxiliary problems
Peter Stone and Manuela Veloso · 1994
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Temporal difference learning and td-gammon
Gerald Tesauro · 1995
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Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Minoru Asada, Shoichi Noda, Sukoya Tawaratsumida, and Koh Hosoda · 1996
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Progressive learning and its application to robot impedance learning
Boo-Ho Yang and Haruhiko Asada · 1996
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Multitask learning
Rich Caruana · 1997
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Child: A first step towards continual learning
Mark B Ring · 1997
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New methods for competitive coevolution
Christopher D Rosin and Richard K Belew · 1997
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Learning from demonstration
Stefan Schaal · 1997
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Multilayered reinforcement learning for complicated collision avoidance problems
Teruo Fujii, Yoshikazu Arai, Hajime Asama, and Isao Endo · 1998
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Tabu search
Fred Glover and Manuel Laguna · 1998
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Reinforcement Learning: An Introduction
Richard Sutton and Andrew Barto · 1998
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Lifelong learning algorithms
Sebastian Thrun · 1998
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Language acquisition in the absence of explicit negative evidence: How important is starting small?
Douglas LT Rohde and David C Plaut · 1999
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An experience applying reinforcement learning in a web-based adaptive and intelligent educational system
Ana Iglesias, Paloma Martínez, and Fernando Fernández · 2003
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A day of great illumination: B. F. Skinner’s discovery of shaping
Gail B Peterson · 2004
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Evolving neural network agents in the nero video game
Kenneth O Stanley, Bobby D Bryant, and Risto Miikkulainen · 2005
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Behavior transfer for value-function-based reinforcement learning
Matthew E Taylor and Peter Stone · 2005
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Using homomorphisms to transfer options across continuous reinforcement learning domains
Vishal Soni and Satinder Singh · 2006
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Reinforcement learning with human teachers: Evidence of feedback and guidance with implications for learning performance
Andrea Lockerd Thomaz and Cynthia Breazeal · 2006
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Transfer learning via inter-task mappings for temporal difference learning
Matthew E Taylor, Peter Stone, and Yaxin Liu · 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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Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing e-Learning
Beverly Park Woolf · 2007
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Transfer of samples in batch reinforcement learning
Alessandro Lazaric, Marcello Restelli, and Andrea Bonarini · 2008
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Autonomous transfer for reinforcement learning
Matthew E Taylor, Gregory Kuhlmann, and Peter Stone · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Learning teaching strategies in an adaptive and intelligent educational system through reinforcement learning
Ana Iglesias, Paloma Martínez, Ricardo Aler, and Fernando Fernández · 2009
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Interactively shaping agents via human reinforcement: The TAMER framework
W Bradley Knox and Peter Stone · 2009
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Assisting transfer-enabled machine learning algorithms: Leveraging human knowledge for curriculum design
Matthew E Taylor · 2009
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Transfer learning for reinforcement learning domains: A survey
Matthew E Taylor and Peter Stone · 2009
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Probabilistic policy reuse for inter-task transfer learning
Fernando Fernández, Javier García, and Manuela Veloso · 2010
Cited alongside, same era.
Partially observable sequential decision making for problem selection in an intelligent tutoring system
Emma Brunskill and Stuart Russell · 2011
Cited alongside, same era.
Learning a skill-teaching curriculum with dynamic Bayes nets
Derek T Green, Thomas J Walsh, Paul R Cohen, and Yu-Han Chang · 2011
Cited alongside, same era.
How do humans teach: On curriculum learning and teaching dimension
Faisal Khan, Bilge Mutlu, and Xiaojin Zhu · 2011
Cited alongside, same era.
Transfer from multiple MDPs
Alessandro Lazaric and Marcello Restelli · 2011
Cited alongside, same era.
Effect of human guidance and state space size on interactive reinforcement learning
Halit Bener Suay and Sonia Chernova · 2011
Reverse curriculum generation for reinforcement learning
Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, and Pieter Abbeel · 2017
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Automated curriculum learning for neural networks
Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu · 2017
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Data-efficient policy evaluation through behavior policy search
Josiah Hanna, Philip Thomas, Peter Stone, and Scott Niekum · 2017
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Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2017
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Faster reinforcement learning using active simulators
Vikas Jain and Theja Tulabandhula · 2017
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Interactive learning from policy-dependent human feedback
James MacGlashan, Mark K Ho, Robert Loftin, Bei Peng, Guan Wang, David L Roberts, Matthew E Taylor, and Michael L Littman · 2017
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Cited alongside, same era.
Curriculum learning for motor skills
Andrej Karpathy and Michiel Van De Panne · 2012
Cited alongside, same era.
Reinforcement learning from simultaneous human and MDP reward
W Bradley Knox and Peter Stone · 2012
Cited alongside, same era.
Transfer in reinforcement learning: a framework and a survey
Alessandro Lazaric · 2012
Cited alongside, same era.
Active learning of inverse models with intrinsically motivated goal exploration in robots
Adrien Baranes and Pierre-Yves Oudeyer · 2013
Cited alongside, same era.
The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Cited alongside, same era.
Transferring task models in reinforcement learning agents
Anestis Fachantidis, Ioannis Partalas, Grigorios Tsoumakas, and Ioannis Vlahavas · 2013
Cited alongside, same era.
Later among the works it cites.
Teacher-student curriculum learning
Tambet Matiisen, Avital Oliver, Taco Cohen, and John Schulman · 2017
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Autonomous task sequencing for customized curriculum design in reinforcement learning
Sanmit Narvekar, Jivko Sinapov, and Peter Stone · 2017
Later among the works it cites.
Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
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Automatic curriculum graph generation for reinforcement learning agents
Maxwell Svetlik, Matteo Leonetti, Jivko Sinapov, Rishi Shah, Nick Walker, and Peter Stone · 2017
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A deep hierarchical approach to lifelong learning in minecraft
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J Mankowitz, and Shie Mannor · 2017
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Training agent for first-person shooter game with actor-critic curriculum learning
Yuxin Wu and Yuandong Tian · 2017
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Emergent complexity via multi-agent competition
Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, and Igor Mordatch · 2018
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Object-oriented curriculum generation for reinforcement learning
Felipe Leno Da Silva and Anna Reali Costa · 2018
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Learning to teach
Yang Fan, Fei Tian, Tao Qin, Xiang-Yang Li, and Tie-Yan Liu · 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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Screenernet: Learning self-paced curriculum for deep neural networks
Tae-Hoon Kim and Jonghyun Choi · 2018
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Overlapping layered learning
Patrick MacAlpine and Peter Stone · 2018
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Curriculum design for machine learners in sequential decision tasks
Bei Peng, James MacGlashan, Robert Loftin, Michael L Littman, David L Roberts, and Matthew E Taylor · 2018
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Self-paced prioritized curriculum learning with coverage penalty in deep reinforcement learning
Zhipeng Ren, Daoyi Dong, Huaxiong Li, and Chunlin Chen · 2018
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Learning by playing solving sparse reward tasks from scratch
Martin 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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Starcraft micromanagement with reinforcement learning and curriculum transfer learning
Kun Shao, Yuanheng Zhu, and Dongbin Zhao · 2018
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Intrinsic motivation and automatic curricula via asymmetric self-play
Sainbayar Sukhbaatar, Zeming Li, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, and Rob Fergus · 2018
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Theory of curriculum learning, with convex loss functions
Daphna Weinshall and Dan Amir · 2018
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Curriculum learning by transfer learning: Theory and experiments with deep networks
Daphna Weinshall, Gad Cohen, and Dan Amir · 2018
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Developmental reinforcement learning through sensorimotor space enlargement
Matthieu Zimmer, Yann Boniface, and Alain Dutech · 2018
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Curriculum-guided hindsight experience replay
Meng Fang, Tianyi Zhou, Yali Du, Lei Han, and Zhengyou Zhang · 2019
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Evolutionarily-curated curriculum learning for deep reinforcement learning agents
Michael Cerny Green, Benjamin Sergent, Pushyami Shandilya, and Vibhor Kumar · 2019
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Barc: Backward reachability curriculum for robotic reinforcement learning
Boris Ivanovic, James Harrison, Apoorva Sharma, Mo Chen, and Marco Pavone · 2019
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Sample-efficient deep reinforcement learning via episodic backward update
Su Young Lee, Choi Sungik, and Sae-Young Chung · 2019
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Learning curriculum policies for reinforcement learning
Sanmit Narvekar and Peter Stone · 2019
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Automated curriculum generation through setter-solver interactions
Sebastien Racaniere, Andrew Lampinen, Adam Santoro, David Reichert, Vlad Firoiu, and Timothy Lillicrap · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Emergent tool use from multi-agent autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, and Igor Mordatch · 2020
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Curriculum learning with a progression function
Andrea Bassich, Francesco Foglino, Matteo Leonetti, and Daniel Kudenko · 2020
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Generalizing curricula for reinforcement learning
Sanmit Narvekar and Peter Stone · 2020
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From few to more: Large-scale dynamic multiagent curriculum learning
Weixun Wang, Tianpei Yang, Yong Liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, and Yang Gao · 2020
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Cm3: Cooperative multi-goal multi-stage multi-agent reinforcement learning
Jiachen Yang, Alireza Nakhaei, David Isele, Kikuo Fujimura, and Hongyuan Zha · 2020
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