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We are interested in the autonomous acquisition of repertoires of skills.
A hitchhiker’s guide to statistical comparisons of reinforcement learning algorithms
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Learning to control a low-cost manipulator using data-efficient reinforcement learning
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Approaching the symbol grounding problem with probabilistic graphical models
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On the spatial foundations of the conceptual system and its enrichment
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Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Universal value function approximators
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Natural language acquisition and grounding for embodied robotic systems
Muhannad Alomari, Paul Duckworth, David C Hogg, and Anthony G Cohn · 2017
Multi-goal reinforcement learning: Challenging robotics environments and request for research
Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, et al · 2018
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Neural task programming: Learning to generalize across hierarchical tasks
Danfei Xu, Suraj Nair, Yuke Zhu, Julian Gao, Animesh Garg, Li Fei-Fei, and Silvio Savarese · 2018
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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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Self-educated language agent with hindsight experience replay for instruction following
Geoffrey Cideron, Mathieu Seurin, Florian Strub, and Olivier Pietquin · 2019
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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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Intrinsically motivated goal exploration processes with automatic curriculum learning
Sébastien Forestier, Yoan Mollard, and Pierre-Yves Oudeyer · 2017
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Grounded language learning in a simulated 3d world
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Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Learning to understand goal specifications by modelling reward
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
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Justin Fu, Anoop Korattikara, Sergey Levine, and Sergio Guadarrama · 2019
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Language as an abstraction for hierarchical deep reinforcement learning
Yiding Jiang, Shixiang Shane Gu, Kevin P Murphy, and Chelsea Finn · 2019
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Towards practical multi-object manipulation using relational reinforcement learning
Richard Li, Allan Jabri, Trevor Darrell, and Pulkit Agrawal · 2019
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A survey of reinforcement learning informed by natural language
Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, and Tim Rocktäschel · 2019
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Contextual imagined goals for self-supervised robotic learning
Ashvin Nair, Shikhar Bahl, Alexander Khazatsky, Vitchyr Pong, Glen Berseth, and Sergey Levine · 2019
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Language as a cognitive tool to imagine goals in curiosity-driven exploration
Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Ford Dominey, and Pierre-Yves Oudeyer · 2020
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Deep sets for generalization in RL
Tristan Karch, Cédric Colas, Laetitia Teodorescu, Clément Moulin-Frier, and Pierre-Yves Oudeyer · 2020
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Corey Lynch and Pierre Sermanet · 2020
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Automatic curriculum learning for deep rl: A short survey
Rémy Portelas, Cédric Colas, Lilian Weng, Katja Hofmann, and Pierre-Yves Oudeyer · 2020
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