Reading List


Please note that lifelong learning has close ties to transfer learning, multi-task learning, curriculum learning, and many other areas.  Due to the extent of these other topics, we omit them from this listing, and instead focus solely on works related to lifelong learning.  For a survey of transfer learning methods, see:

 

Books

 

Surveys

  • Lifelong Machine Learning Systems: Beyond Learning AlgorithmsDaniel L. Silver, Qiang Yang, and Lianghao Li, 2013.
    • Surveys some of the key issues in lifelong learning, covering techniques through early 2013.  For a more up-to-date treatment of the issues, see either Chen and Liu’s book and tutorials, or the papers listed below.

 

Dissertations and Theses

 

Lifelong Learning for Classification and Regression

 

Lifelong Reinforcement Learning

 

Few-Shot Transfer in Lifelong Learning

 

Deep Lifelong Learning

 

Lifelong Learning for User Modeling and Crowdsourcing

 

Lifelong Robot Learning

 

Lifelong Learning of Structured Information

 

Testbeds and Data Sets

  • DeepMind Lab.  Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, & Stig Petersen.  arXiv 2016. 
  • OpenAI Gym