Effects of Reward Terms in Agent-Based Box-Manipulation Animation Using Deep Reinforcement Learning

Hsiang Yu Yang, Chien Chou Wong, Sai-Keung Wong

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Agent-based box manipulation has wide applications in computer animation and robotics. Deep reinforcement learning can be applied to generate animations of agent-based box manipulation. This paper focuses on push-manipulation in an agent-based animation. A policy is learned in a learning session in which an agent receives a reward that is a combination of different types of reward terms. Based on the received reward, the policy is improved gradually. In this paper, we investigate the effects of each reward term in-depth in a framework that is integrated with deep reinforcement learning. We also propose a simple way to produce different animation types. We performed several examples and analyzed our findings in details.

原文English
主出版物標題Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁數6
ISBN(電子)9781728146669
DOIs
出版狀態Published - 21 11月 2019
事件24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 - Kaohsiung, Taiwan
持續時間: 21 11月 201923 11月 2019

出版系列

名字Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019

Conference

Conference24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019
國家/地區Taiwan
城市Kaohsiung
期間21/11/1923/11/19

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