每年專案
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??? |
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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 2019 → 9 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 |
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國家/地區 | Indonesia |
城市 | Bali |
期間 | 6/08/19 → 9/08/19 |
指紋
深入研究「The matter of deep reinforcement learning towards practical ai applications」主題。共同形成了獨特的指紋。專案
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