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In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals.
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IM Gel’Fand and AM Yaglom · 1959
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Learning by playing solving sparse reward tasks from scratch
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Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play
Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, and Rob Fergus · 2018
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Learning quickly to plan quickly using modular meta-learning
Rohan Chitnis, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 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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Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2019
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Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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EMI: Exploration with Mutual Information
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Alexandre Galashov, Siddhant Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojtek M. Czarnecki, Yee Whye Teh, Razvan Pascanu, and Nicolas Heess · 2019
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Transfer and Exploration via the Information Bottleneck
Anirudh Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Sergey Levine, and Yoshua Bengio · 2019
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Self-supervised exploration via disagreement
Deepak Pathak, Dhiraj Gandhi, and Abhinav Gupta · 2019
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Episodic Curiosity through Reachability
Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphael Marinier, Marc Pollefeys, Timothy Lillicrap, and Sylvain Gelly · 2019
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Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O Stanley · 2019
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Dynamics-Aware Unsupervised Skill Discovery
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2020
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