A domain-agnostic approach for characterization of lifelong learning systems

Author:

Baker Megan M.ORCID,New Alexander,Aguilar-Simon Mario,Al-Halah Ziad,Arnold Sébastien M.R.,Ben-Iwhiwhu Ese,Brna Andrew P.,Brooks Ethan,Brown Ryan C.,Daniels Zachary,Daram Anurag,Delattre Fabien,Dellana Ryan,Eaton Eric,Fu Haotian,Grauman Kristen,Hostetler Jesse,Iqbal Shariq,Kent Cassandra,Ketz Nicholas,Kolouri Soheil,Konidaris George,Kudithipudi Dhireesha,Learned-Miller Erik,Lee Seungwon,Littman Michael L.,Madireddy Sandeep,Mendez Jorge A.,Nguyen Eric Q.,Piatko Christine,Pilly Praveen K.,Raghavan Aswin,Rahman Abrar,Ramakrishnan Santhosh Kumar,Ratzlaff Neale,Soltoggio Andrea,Stone Peter,Sur Indranil,Tang Zhipeng,Tiwari Saket,Vedder Kyle,Wang Felix,Xu Zifan,Yanguas-Gil Angel,Yedidsion Harel,Yu Shangqun,Vallabha Gautam K.

Funder

Defense Advanced Research Projects Agency

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference113 articles.

1. Agarwal, R., Schwarzer, M., Castro, P. S., Courville, A., & Bellemare, M. G. (2021). Deep reinforcement learning at the edge of the statistical precipice. In Thirty-fifth conference on neural information processing systems.

2. When MAML can adapt fast and how to assist when it cannot;Arnold,2021

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4. Learning sparse feature representations using probabilistic quadtrees and deep belief nets;Basu;Neural Processing Letters,2017

5. The Arcade learning environment: An evaluation platform for general agents;Bellemare;Journal of Artificial Intelligence Research,2013

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