# Learning Good Representation via Continuous Attention

## TL;DR

This paper introduces a novel approach where continuous attention driven by intrinsic rewards from unsupervised learning enhances representation learning, improving object recognition in reinforcement learning agents.

## Contribution

The paper proposes a new method combining unsupervised learning and reinforcement learning with intrinsic rewards to improve object representation learning.

## Key findings

- Effective in few-shot object recognition tasks
- Improves representation quality via continuous attention
- Works with and without extrinsic rewards

## Abstract

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.