The matter of deep reinforcement learning towards practical ai applications

Tipajin Thaipisutikul, Yi Cheng Chen, Lin Hui, Sheng Chih Chen, Pattanasak Mongkolwat, Timothy K. Shih

研究成果: 書貢獻/報告類型會議論文篇章同行評審

4 引文 斯高帕斯(Scopus)

摘要

Reinforcement Learning (RL) is an extraordinarily paradigm that aims to solve a complex problem. This technique leverages the traditional feedforward networks with temporal-difference learning to overcome supervised and unsupervised real-world problems. However, RL is one of state-of-the-art topic due to the opaque aspects in design and implementation. Also, in which situation we will get performance gain from RL is still unclear. Therefore, This study firstly examines the impact of Experience Replay in Deep Q-Learning agent with Self-Driving Car application. Secondly, The impact of Eligibility Trace in RNN A3C agents with Breakout AI game application is studied. Our results indicated that these two techniques enhance RL performance by more than 20 percent as compared with traditional RL methods.

原文???core.languages.en_GB???
主出版物標題Proceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面24-29
頁數6
ISBN(電子)9781728128207
DOIs
出版狀態已出版 - 8月 2019
事件12th International Conference on Ubi-Media Computing, Ubi-Media 2019 - Bali, Indonesia
持續時間: 6 8月 20199 8月 2019

出版系列

名字Proceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019

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???event.eventtypes.event.conference???12th International Conference on Ubi-Media Computing, Ubi-Media 2019
國家/地區Indonesia
城市Bali
期間6/08/199/08/19

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