Access Type
Open Access Embargo
Date of Award
January 2019
Degree Type
Dissertation
Degree Name
Ph.D.
Department
Computer Science
First Advisor
Xuewen . Chen
Abstract
Attention mechanism has shown promising results in many fields of machine learning such as image captioning and machine translation. In this work, we focus on attention-based models for deep reinforcement learning. We concentrate on developing deep neural networks
that are fed with a sequence of high-dimensional raw pixels. Particularly, we design attention-based models for challenging tasks including navigation, autonomous driving, and video captioning. In these tasks, deep reinforcement learning algorithms facilitate training of their sophisticated models, and the attention mechanism serves different purposes. In the navigation and autonomous driving tasks, through the attention mechanism, our model attends over different views of the environment provided by different available cameras to decide about the
best actions that should be taken at each time step. On the other hand, in the video captioning task, through the attention mechanism, the model attends over correlated events to generate captions for different temporal regions in the video. Through experiments, we illustrate that the performance of our attention-based methods for deep reinforcement learning surpasses the performance of their state-of-the-art baselines.
Recommended Citation
Barati, Elaheh, "Attention-Based Models For Deep Reinforcement Learning" (2019). Wayne State University Dissertations. 2345.
https://digitalcommons.wayne.edu/oa_dissertations/2345