標題: | Combining Deep Deterministic Policy Gradient with Cross-Entropy Method |
作者: | Lai, Tung-Yi Hsueh, Chu-Hsuan Lin, You-Hsuan Chu, Yeong-Jia Roger Hsueh, Bo-Yang Wu, I-Chen 資訊工程學系 Department of Computer Science |
關鍵字: | reinforcement learning;robotics;object grasping;deep deterministic policy gradient;cross-entropy method |
公開日期: | 1-一月-2019 |
摘要: | This paper proposes a deep reinforcement learning algorithm for solving robotic tasks, such as grasping objects. We propose in this paper a combination of cross-entropy optimization (CE) with deep deterministic policy gradient (DDPG). More specifically, where in the CE method, we first sample from a Gaussian distribution with zero as its initial mean, we now set the initial mean to DDPG's output instead. The resulting algorithm is referred to as the DDPG-CE method. Next, to negate the effects of bad samples, we improve on DDPG-CE by substituting the CE component with a weighted CE method, resulting in the DDPG-WCE algorithm. Experiments show that DDPG-WCE achieves a higher success rate on grasping previously unseen objects, than other approaches, such as supervised learning, DDPG, CE, and DDPG-CE. |
URI: | http://hdl.handle.net/11536/154061 |
ISBN: | 978-1-7281-4666-9 |
ISSN: | 2376-6816 |
期刊: | 2019 INTERNATIONAL CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI) |
起始頁: | 0 |
結束頁: | 0 |
顯示於類別: | 會議論文 |