# Reinforcement Learning with Attention that Works: A Self-Supervised Approach

## TL;DR

This paper introduces a novel self-supervised attention mechanism integrated with reinforcement learning, achieving significant performance improvements and state-of-the-art results in complex environments by dynamically focusing on multiple relevant aspects of the input.

## Contribution

It presents the first successful combination of self-attention with reinforcement learning, leveraging Markovian properties for improved decision-making and interpretability.

## Key findings

- Achieved new state-of-the-art results in Arcade Learning Environment.
- Demonstrated policies using multiple simultaneous attention foci.
- Showed attention modulation over time for partial observability.

## Abstract

Attention models have had a significant positive impact on deep learning across a range of tasks. However previous attempts at integrating attention with reinforcement learning have failed to produce significant improvements. We propose the first combination of self attention and reinforcement learning that is capable of producing significant improvements, including new state of the art results in the Arcade Learning Environment. Unlike the selective attention models used in previous attempts, which constrain the attention via preconceived notions of importance, our implementation utilises the Markovian properties inherent in the state input. Our method produces a faithful visualisation of the policy, focusing on the behaviour of the agent. Our experiments demonstrate that the trained policies use multiple simultaneous foci of attention, and are able to modulate attention over time to deal with situations of partial observability.