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Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems, trains an agent to achieve different goals under particular scenarios.
Efficient exploratory learning of inverse kinematics on a bionic elephant trunk
Matthias Rolf and Jochen J Steil · 2013
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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Charles Beattie, Joel Z Leibo, et al · 2016
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Greg Brockman, Vicki Cheung, et al · 2016
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Modular active curiosity-driven discovery of tool use
Sébastien Forestier and Pierre-Yves Oudeyer · 2016
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The malmo platform for artificial intelligence experimentation
Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell · 2016
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Grail: a goal-discovering robotic architecture for intrinsically-motivated learning
Vieri Giuliano Santucci, Gianluca Baldassarre, and Marco Mirolli · 2016
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
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Ltlf/ldlf non-markovian rewards
Ronen Brafman, Giuseppe De Giacomo, and Fabio Patrizi · 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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Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 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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Multi-goal reinforcement learning: Challenging robotics environments and request for research
Matthias Plappert, Marcin Andrychowicz, et al · 2018
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Special issue on goal reasoning
Mark Roberts, Daniel Borrajo, Michael Cox, and Neil Yorke-Smith · 2018
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Universal planning networks: Learning generalizable representations for visuomotor control
Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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The laplacian in rl: Learning representations with efficient approximations
Yifan Wu, George Tucker, and Ofir Nachum · 2018
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Guided goal generation for hindsight multi-goal reinforcement learning
Chenjia Bai, Peng Liu, Wei Zhao, and Xianglong Tang · 2019
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Actrce: Augmenting experience via teacher’s advice for multi-goal reinforcement learning
Harris Chan, Yuhuai Wu, Jamie Kiros, Sanja Fidler, and Jimmy Ba · 2019
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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2019
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Search on the replay buffer: Bridging planning and reinforcement learning
Benjamin Eysenbach, Ruslan Salakhutdinov, and Sergey Levine · 2019
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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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Dynamical distance learning for semi-supervised and unsupervised skill discovery
Kristian Hartikainen, Xinyang Geng, Tuomas Haarnoja, and Sergey Levine · 2019
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Language as an abstraction for hierarchical deep reinforcement learning
Yiding Jiang, Shixiang Gu, Kevin Murphy, and Chelsea Finn · 2019
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Aviral Kumar, Xue Bin Peng, and Sergey Levine · 2019
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A survey of reinforcement learning informed by natural language
Jelena Luketina, Nantas Nardelli, et al · 2019
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Goal reasoning in the clips executive for integrated planning and execution
Tim Niemueller, Till Hofmann, and Gerhard Lakemeyer · 2019
Cited alongside, same era.
Learning from trajectories via subgoal discovery
Skew-fit: State-covering self-supervised reinforcement learning
Vitchyr H Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine · 2020
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Dynamics-aware unsupervised discovery of skills
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2020
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dm_control: Software and tasks for continuous control, 2020
Yuval Tassa, Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, and Nicolas Heess · 2020
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Automatic curriculum learning through value disagreement
Yunzhi Zhang, Pieter Abbeel, and Lerrel Pinto · 2020
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Grounding language to autonomously-acquired skills via goal generation
Ahmed Akakzia, Cédric Colas, Pierre-Yves Oudeyer, Mohamed Chetouani, and Olivier Sigaud · 2021
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Learning with amigo: Adversarially motivated intrinsic goals
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Sujoy Paul, Jeroen Vanbaar, and Amit Roy-Chowdhury · 2019
Cited alongside, same era.
Exploration via hindsight goal generation
Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, and Jian Peng · 2019
Cited alongside, same era.
Reinforcement learning upside down: Don’t predict rewards–just map them to actions
Juergen Schmidhuber · 2019
Cited alongside, same era.
Training agents using upside-down reinforcement learning
Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, and Jürgen Schmidhuber · 2019
Cited alongside, same era.
Keeping your distance: Solving sparse reward tasks using self-balancing shaped rewards
Alexander Trott, Stephan Zheng, Caiming Xiong, and Richard Socher · 2019
Cited alongside, same era.
Unsupervised control through non-parametric discriminative rewards
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, and Volodymyr Mnih · 2019
Cited alongside, same era.
Plangan: Model-based planning with sparse rewards and multiple goals
Henry Charlesworth and Giovanni Montana · 2020
Cited alongside, same era.
Andres Campero, Roberta Raileanu, Heinrich Küttler, Joshua B Tenenbaum, Tim Rocktäschel, and Edward Grefenstette · 2021
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Goal-conditioned reinforcement learning with imagined subgoals
Elliot Chane-Sane, Cordelia Schmid, and Ivan Laptev · 2021
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Actionable models: Unsupervised offline reinforcement learning of robotic skills
Yevgen Chebotar, Karol Hausman, et al · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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Glib: Efficient exploration for relational model-based reinforcement learning via goal-literal babbling
Rohan Chitnis, Tom Silver, Joshua Tenenbaum, Leslie Pack Kaelbling, and Tomas Lozano-Perez · 2021
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Epidemioptim: A toolbox for the optimization of control policies in epidemiological models
Cédric Colas, Boris Hejblum, Sébastien Rouillon, et al · 2021
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Adversarial intrinsic motivation for reinforcement learning
Ishan Durugkar, Mauricio Tec, Scott Niekum, and Peter Stone · 2021
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Learning to reach goals via iterated supervised learning
Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Devin, Benjamin Eysenbach, and Sergey Levine · 2021
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What can i do here? learning new skills by imagining visual affordances
Alexander Khazatsky, Ashvin Nair, Daniel Jing, and Sergey Levine · 2021
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Imaginary hindsight experience replay: Curious model-based learning for sparse reward tasks
Robert McCarthy and Stephen J Redmond · 2021
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Discovering and achieving goals via world models
Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, and Deepak Pathak · 2021
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Rapid exploration for open-world navigation with latent goal models
Dhruv Shah, Benjamin Eysenbach, Nicholas Rhinehart, and Sergey Levine · 2021
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Hindsight expectation maximization for goal-conditioned reinforcement learning
Yunhao Tang and Alp Kucukelbir · 2021
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Model-based visual planning with self-supervised functional distances
Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, and Sergey Levine · 2021
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World model as a graph: Learning latent landmarks for planning
Lunjun Zhang, Ge Yang, and Bradly C Stadie · 2021
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Mapgo: Model-assisted policy optimization for goal-oriented tasks
Menghui Zhu, Minghuan Liu, Jian Shen, Zhicheng Zhang, Sheng Chen, Weinan Zhang, Deheng Ye, Yong Yu, Qiang Fu, and Wei Yang · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, et al · 2022
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