论文标题
自动导航的胶囊网络性能
Capsule Network Performance with Autonomous Navigation
论文作者
论文摘要
已提出胶囊网络（CAPSNET）作为卷积神经网络（CNN）的替代方法。本文展示了在自主剂探索现实场景的自主剂探索方面，瓶颈比CNN的功能更强。在现实世界导航中，代理外部的奖励可能很少。反过来，加强学习算法可能难以形成有意义的政策功能。本文的方法胶囊探索模块（CAPS-EM）将封装架构与优势演员评论家算法配对。导航稀疏环境的其他方法需要内在的奖励发生器，例如内在好奇模块（ICM）和增强好奇心模块（ACM）。 CAPS-EM使用更紧凑的体系结构，而无需内在的奖励。 Tested using ViZDoom, the Caps-EM uses 44% and 83% fewer trainable network parameters than the ICM and Depth-Augmented Curiosity Module (D-ACM), respectively, for 1141% and 437% average time improvement over the ICM and D-ACM, respectively, for converging to a policy function across "My Way Home" scenarios.
Capsule Networks (CapsNets) have been proposed as an alternative to Convolutional Neural Networks (CNNs). This paper showcases how CapsNets are more capable than CNNs for autonomous agent exploration of realistic scenarios. In real world navigation, rewards external to agents may be rare. In turn, reinforcement learning algorithms can struggle to form meaningful policy functions. This paper's approach Capsules Exploration Module (Caps-EM) pairs a CapsNets architecture with an Advantage Actor Critic algorithm. Other approaches for navigating sparse environments require intrinsic reward generators, such as the Intrinsic Curiosity Module (ICM) and Augmented Curiosity Modules (ACM). Caps-EM uses a more compact architecture without need for intrinsic rewards. Tested using ViZDoom, the Caps-EM uses 44% and 83% fewer trainable network parameters than the ICM and Depth-Augmented Curiosity Module (D-ACM), respectively, for 1141% and 437% average time improvement over the ICM and D-ACM, respectively, for converging to a policy function across "My Way Home" scenarios.
