# Attentive Multi-Task Deep Reinforcement Learning

## TL;DR

This paper introduces an attention-based multi-task deep reinforcement learning method that automatically manages knowledge sharing between tasks, promoting positive transfer and avoiding negative interference without prior task relationship assumptions.

## Contribution

It presents a novel attention mechanism that dynamically groups task knowledge at a state level, improving transfer learning efficiency and robustness in multi-task reinforcement learning.

## Key findings

- Achieves comparable or better performance than state-of-the-art methods.
- Requires fewer network parameters.
- Effectively avoids negative transfer between tasks.

## Abstract

Sharing knowledge between tasks is vital for efficient learning in a multi-task setting. However, most research so far has focused on the easier case where knowledge transfer is not harmful, i.e., where knowledge from one task cannot negatively impact the performance on another task. In contrast, we present an approach to multi-task deep reinforcement learning based on attention that does not require any a-priori assumptions about the relationships between tasks. Our attention network automatically groups task knowledge into sub-networks on a state level granularity. It thereby achieves positive knowledge transfer if possible, and avoids negative transfer in cases where tasks interfere. We test our algorithm against two state-of-the-art multi-task/transfer learning approaches and show comparable or superior performance while requiring fewer network parameters.