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Reinforcement Teaching



Alex Lewandowski , Calarina Muslimani , Matthew E. Taylor , Jun Luo , Dale Schuurmans

* First two authors contributed equally 

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Methodical Advice Collection and Reuse in Deep Reinforcement Learning



Sahir , Ercüment İlhan , Srijita Das , Matthew E. Taylor

* To be published in ALA2022: Adaptive and Learning Agents Workshop 2022 at AAMAS 

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PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration



Pengyi Li , Hongyao Tang , Tianpei Yang , Xiaotian Hao , Tong Sang , Yan Zheng , Jianye Hao , Matthew E. Taylor , Zhen Wang

* A preliminary version has been accepted on the Cooperative AI Workshop at 35th Conference on Neural Information Processing Systems (NeurIPS 2021) 

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Learning Representations for Pixel-based Control: What Matters and Why?



Manan Tomar , Utkarsh A. Mishra , Amy Zhang , Matthew E. Taylor


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Multi-Agent Advisor Q-Learning



Sriram Ganapathi Subramanian , Matthew E. Taylor , Kate Larson , Mark Crowley

* New version has some typos corrected 

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The Atari Data Scraper



Brittany Davis Pierson , Justine Ventura , Matthew E. Taylor

* 3 authors, nine pages, 6 figures, papers with code 

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The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning



Volodymyr Tkachuk , Sriram Ganapathi Subramanian , Matthew E. Taylor

* 7 pages, 2 figures, submitted to ALA 2021 

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Model-Invariant State Abstractions for Model-Based Reinforcement Learning



Manan Tomar , Amy Zhang , Roberto Calandra , Matthew E. Taylor , Joelle Pineau


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Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems



Yaodong Yang , Jun Luo , Ying Wen , Oliver Slumbers , Daniel Graves , Haitham Bou Ammar , Jun Wang , Matthew E. Taylor

* AAMAS 2021 

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