One of the most challenging and open problems in Artificial Intelligence (AI) is that of Lifelong Learning:
“Lifelong Learning is the continued learning of tasks, from one or more domains, over the course of a lifetime, by a lifelong learning system. A lifelong learning system efficiently and effectively (1) retains the knowledge it has learned; (2) selectively transfers knowledge to learn new tasks; and (3) ensures the effective and efficient interaction between (1) and (2).”
Lifelong learning is still in its infancy. Many issues currently exist such as learning general representations, catastrophic forgetting, efficient knowledge retention mechanisms and hierarchical abstractions. Much work has been done in the Reinforcement Learning (RL) community to tackle different elements of lifelong learning. Active research topics include hierarchical abstractions, transfer learning, multi-task learning and curriculum learning. With the emergence of powerful function approximators such as in Deep Learning, we feel that now is a perfect time to provide a forum to discuss ways to move forward and provide a truly general lifelong learning framework, using RL-based algorithms, with more rigor than ever before. This workshop will endeavor to promote interaction between researchers working on the different elements of lifelong learning to try and find a synergy between the various techniques.
This is the second edition of this workshop. First edition happened in ICML 2017 and details can be found here.
Sarath Chandar, University of Montreal
Tom Zahavy, Technion
Daniel Mankowitz, Deepmind
Balaraman Ravindran, Indian Institute of Technology Madras
Shie Mannor, Technion