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Open-Ended Learning Leads to Generally Capable Agents


Jul 31, 2021
Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, Nat McAleese, Nathalie Bradley-Schmieg, Nathaniel Wong, Nicolas Porcel, Roberta Raileanu, Steph Hughes-Fitt, Valentin Dalibard, Wojciech Marian Czarnecki


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Decoupling Representation Learning from Reinforcement Learning


Sep 30, 2020
Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin

* Improved related works and fixed code hyperlink 

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Responsive Safety in Reinforcement Learning by PID Lagrangian Methods


Jul 08, 2020
Adam Stooke, Joshua Achiam, Pieter Abbeel

* ICML 2020 

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Perception-Prediction-Reaction Agents for Deep Reinforcement Learning


Jun 26, 2020
Adam Stooke, Valentin Dalibard, Siddhant M. Jayakumar, Wojciech M. Czarnecki, Max Jaderberg


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Reinforcement Learning with Augmented Data


May 11, 2020
Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas

* First two authors contributed equally, website: https://mishalaskin.github.io/rad code: https://github.com/MishaLaskin/rad and https://github.com/pokaxpoka/rad_procgen 

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rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch


Sep 24, 2019
Adam Stooke, Pieter Abbeel

* v2: Updated learning curves for SAC and TD3, improved by bootstrapping value-function when trajectory ends due to time limit, and switching to newer SAC version, now referenced 

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