0) Motivation, Object and Related works:
Motivation:
While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, data efficiency remains a major challenge.
Multi-task learning has emerged as a promising approach for sharing structure across multiple tasks to enable more efficient learning.
Problem:
The multi-task setting presents a number of optimization challenges, making it difficult to realize large efficiency gains compared to learning tasks independently.
The reasons why multi-task learning is so challenging compared to single-task learning are not fully understood.
Objectives:
In this work, we identify a set of three conditions of the multi-task optimization landscape that cause detrimental gradient interference, and develop a simple yet general approach for avoiding such interference between task gradients.
We propose a form of gradient surgery that projects a task's gradient onto the normal plane of the gradient of any other task that has a conflicting gradient. On a series of challenging multi-task supervised and multi-task RL problems, this approach leads to substantial gains in efficiency and performance. Further, it is model-agnostic and can be combined with previously-proposed multi-task architectures for enhanced performance.
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