Title: Generalization Tower Network: A Novel Deep Neural Network Architecture for Multi-Task Learning,
Abstract: Deep learning (DL) advances state-of-the-art reinforcement learning (RL), by
incorporating deep neural networks in learning representations from the input
to RL. However, the conventional deep neural network architecture is limited in
learning representations for multi-task RL (MT-RL), as multiple tasks can refer
to different kinds of representations. In this paper, we thus propose a novel
deep neural network architecture, namely generalization tower network (GTN),
which can achieve MT-RL within a single learned model. Specifically, the
architecture of GTN is composed of both horizontal and vertical streams. In our
GTN architecture, horizontal streams are used to learn representation shared in
similar tasks. In contrast, the vertical streams are introduced to be more
suitable for handling diverse tasks, which encodes hierarchical shared
knowledge of these tasks. The effectiveness of the introduced vertical stream
is validated by experimental results. Experimental results further verify that
our GTN architecture is able to advance the state-of-the-art MT-RL, via being
tested on 51 Atari games.