  In this paper we present our scientific discovery that good representation
can be learned via continuous attention during the interaction between
Unsupervised Learning(UL) and Reinforcement Learning(RL) modules driven by
intrinsic motivation. Specifically, we designed intrinsic rewards generated
from UL modules for driving the RL agent to focus on objects for a period of
time and to learn good representations of objects for later object recognition
task. We evaluate our proposed algorithm in both with and without extrinsic
reward settings. Experiments with end-to-end training in simulated environments
with applications to few-shot object recognition demonstrated the effectiveness
of the proposed algorithm.
