Transfer Learning Through Embedding Spaces by Rostami Mohammad;

Author:Rostami, Mohammad;
Language: eng
Format: epub
Publisher: CRC Press LLC
Published: 2021-04-21T00:00:00+00:00

where . This is equivalent to minimizing the KL divergence between the reward-weighted trajectory distribution of πθ and the trajectory distribution of the new policy .

In our work, we treat the term similar to the loss function of a classification or regression task. Consequently, both supervised learning tasks and RL tasks can be modeled in a unified framework, where the goal is to minimize a convex loss function.

6.3.3 Lifelong Machine Learning

In a lifelong learning setting [243, 211], a learner faces multiple, consecutive tasks and must rapidly learn each new task by building upon its previous experience. The learner may encounter a previous task at any time, and so must optimize performance across all tasks seen so far. A priori, the agent does not know the total number of tasks Tmax, the task distribution, or the task order.

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