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Lifelong reinforcement learning with autonomous inference of subtask dependencies
In this project, we propose a continual learning approach to face the problem of forward transfer in complex reinforcement learning tasks. Concretely, we propose a model that learns how to combine a series of general modules in a deep learning architecture, so that generalization emerges through the composition of those modules. This is of vital importance for Element AI to provide reusable solutions that scale with new data, without the need of learning a new model for every problem and improving the overall performance.
Faculty Supervisor:
Laurent Charlin
Student:
Partner:
ServiceNow Canada
Discipline:
Computer science
Sector:
Professional, scientific and technical services
University:
HEC Montréal
Program:
Accelerate
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