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ICASSP 2019accepted0 citations

Non-local Self-attention Structure for Function Approximation in Deep Reinforcement Learning

Zhixiang Wang, Xi Xiao, Guangwu Hu, Yao Yao, Dianyan Zhang, Zhendong Peng, Qing Li, Shutao Xia

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Abstract

Reinforcement learning is a framework to make sequential decisions. The combination with deep neural networks further improves the ability of this framework. Convolutional nerual networks make it possible to make sequential decisions based on raw pixels information directly and make reinforcement learning achieve satisfying performances in series of tasks. However, convolutional neural networks still have own limitations in representing geometric patterns and long-term dependencies that occur consistently in state inputs. To tackle with the limitation, we propose the self-attention architecture to augment the original network. It provides a better balance between ability to model long-range dependencies and computational efficiency. Experiments on Atari games illustrate that self-attention structure is significantly effective for function approximation in deep reinforcement learning.

BibTeX
@inproceedings{icassp2019_nonlocalselfatte,
  title = {Non-local Self-attention Structure for Function Approximation in Deep Reinforcement Learning},
  author = {Zhixiang Wang and Xi Xiao and Guangwu Hu and Yao Yao and Dianyan Zhang and Zhendong Peng and Qing Li and Shutao Xia},
  booktitle = {ICASSP 2019},
  year = {2019}
}
Non-local Self-attention Structure for Function Approximation in Deep Reinforcement Learning · ICASSP 2019