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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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Accelerated Methods for Deep Reinforcement Learning

Mar 07, 2018
Adam Stooke, Pieter Abbeel


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#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

Dec 05, 2017
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

* 10 pages main text + 10 pages supplementary. Published at NIPS 2017 

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Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

Oct 11, 2017
Adam Stooke, Pieter Abbeel


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