Abstract
Swarm robotic systems (SRS) are a type of multi-robot systems that consist of many homogeneous autonomous robots inspired by social insects. In our pervious study, we succeeded in developing end-to-end control policies for SRS using Deep Q-Network (DQN) algorithm. However, since DQN is totally a black box, it is difficult to understand what were learnt through the learning process. Therefore, in this paper, a novel method of visualizing the decision making process in the DQN is proposed by combining Deconvolutional Network (Deconvnet) and Gradient-weighted Class Activation Mapping (Grad-CAM). Then we show what are being preserved as the deep features and which part of input image is concerned to make an action decision. The proposed method is demonstrated by conducting the computer simulations of a round trip task, in which the swarm robots need to visit two different locations alternatively as many times as possible. The computer simulations might also be explained that the proposed method visualizes the policies learned by DQN.
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Nie, X., Hiraga, M., Ohkura, K. (2020). Visualizing Deep Q-Learning to Understanding Behavior of Swarm Robotic System. In: Sato, H., Iwanaga, S., Ishii, A. (eds) Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems. IES 2019. Proceedings in Adaptation, Learning and Optimization, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-37442-6_11
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