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-Jan-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
Appears in Collections:Conferences Paper