Transfer Learning in Reinforcement Learning

Edwin Torres
Luis Avedaño
Funded by MinCiencias

Knowledge transfer is a feature of the human learning processthat machine learning algorithms are capable of imitating. The objective in these cases is to use knowledge acquired in previously learned tasks to improve learning performance on a new task. Although different transfer techniques have been developed in reinforcement learning, few techniques have focused on the reproduction of the memory units involved in knowledge transfer performed by humans. This project propose novel methods that facilitates the transfer by means of a memory unit when an agent is learning to solve an unknown task that is more difficult than previously learned tasks.


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