Workshop on Lifelong Learning: A Reinforcement Learning Approach @ICML 2017
LL@ICML 2017
- URL: http://rlabstraction2016.wixsite.com/icml-2017
- Event Date: 2017-08-10 ~ 2017-08-10
- Submission Date: 2017-06-06
- Location: Sydney, Australia
Dear Colleagues,
We would like to invite you to submit extended abstracts of between 4-6 pages to our 'Lifelong Learning: A Reinforcement Learning Approach' ICML 2017 Workshop which will be held in Sydney, Australia on August 10, 2017.
IMPORTANT INFORMATION
************************************************
Website: http://rlabstraction2016.wix.com/icml-2017
Date: 10 August 2017
Location: Sydney, Australia
Submission deadline: 6th June 2017, 11:59 PM (GMT+2)
OVERVIEW
************************************************
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 rigour than ever before. This workshop will endeavour to promote interaction between researchers working on the different elements of lifelong learning to try and find a synergy between the various techniques.
SUBMISSION
************************************************
The submitted work should be an extended abstract of between 4-6 pages (including references). The submission should be in pdf format and should follow the style guidelines for ICML 2017. The review process is double-blind and the work should be submitted by the latest 6th June 2017, 11:59 PM(GMT+2). Submissions must be made through easychair: https://easychair.org/conferences/?conf=llicml2017
AREAS OF INTEREST
************************************************
Using Hierarchical Abstractions to perform lifelong learning (e.g., skills/options and state space representations)
Transfer Learning
Multi-task Learning
Curriculum Learning
Deep Learning as a tool for performing lifelong learning
Determine new, challenging benchmark domains
For more info see our website.
WORKSHOP ORGANIZERS
************************************************
Sarath Chandar - University of Montreal
Balaraman Ravindran - Indian Institute of Technology
Daniel J. Mankowitz - Technion Israel Institute of Technology
Tom Zahavy - Technion Israel Institute of Technology
Shie Mannor - Technion Israel Institute of Technology
We look forward to reviewing your submissions and hope to see you in Sydney!
Kind regards,
Sarath, Ravi, Daniel, Tom and Shie
Lifelong Learning: A Reinforcement Learning Approach Workshop organizers
We would like to invite you to submit extended abstracts of between 4-6 pages to our 'Lifelong Learning: A Reinforcement Learning Approach' ICML 2017 Workshop which will be held in Sydney, Australia on August 10, 2017.
IMPORTANT INFORMATION
************************************************
Website: http://rlabstraction2016.wix.com/icml-2017
Date: 10 August 2017
Location: Sydney, Australia
Submission deadline: 6th June 2017, 11:59 PM (GMT+2)
OVERVIEW
************************************************
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 rigour than ever before. This workshop will endeavour to promote interaction between researchers working on the different elements of lifelong learning to try and find a synergy between the various techniques.
SUBMISSION
************************************************
The submitted work should be an extended abstract of between 4-6 pages (including references). The submission should be in pdf format and should follow the style guidelines for ICML 2017. The review process is double-blind and the work should be submitted by the latest 6th June 2017, 11:59 PM(GMT+2). Submissions must be made through easychair: https://easychair.org/conferences/?conf=llicml2017
AREAS OF INTEREST
************************************************
Using Hierarchical Abstractions to perform lifelong learning (e.g., skills/options and state space representations)
Transfer Learning
Multi-task Learning
Curriculum Learning
Deep Learning as a tool for performing lifelong learning
Determine new, challenging benchmark domains
For more info see our website.
WORKSHOP ORGANIZERS
************************************************
Sarath Chandar - University of Montreal
Balaraman Ravindran - Indian Institute of Technology
Daniel J. Mankowitz - Technion Israel Institute of Technology
Tom Zahavy - Technion Israel Institute of Technology
Shie Mannor - Technion Israel Institute of Technology
We look forward to reviewing your submissions and hope to see you in Sydney!
Kind regards,
Sarath, Ravi, Daniel, Tom and Shie
Lifelong Learning: A Reinforcement Learning Approach Workshop organizers
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